diff --git a/.astro/data-store.json b/.astro/data-store.json
index c4d4215..4d77db7 100644
--- a/.astro/data-store.json
+++ b/.astro/data-store.json
@@ -1 +1 @@
-[["Map",1,2,7,8],"meta::meta",["Map",3,4,5,6],"astro-version","5.12.6","astro-config-digest","{\"root\":{},\"srcDir\":{},\"publicDir\":{},\"outDir\":{},\"cacheDir\":{},\"compressHTML\":true,\"base\":\"/\",\"trailingSlash\":\"ignore\",\"output\":\"server\",\"scopedStyleStrategy\":\"attribute\",\"build\":{\"format\":\"directory\",\"client\":{},\"server\":{},\"assets\":\"_astro\",\"serverEntry\":\"entry.mjs\",\"redirects\":true,\"inlineStylesheets\":\"auto\",\"concurrency\":1},\"server\":{\"open\":false,\"host\":true,\"port\":4321,\"streaming\":true,\"allowedHosts\":[]},\"redirects\":{},\"image\":{\"endpoint\":{\"route\":\"/_image\",\"entrypoint\":\"astro/assets/endpoint/node\"},\"service\":{\"entrypoint\":\"astro/assets/services/sharp\",\"config\":{}},\"domains\":[],\"remotePatterns\":[],\"responsiveStyles\":false},\"devToolbar\":{\"enabled\":true},\"markdown\":{\"syntaxHighlight\":{\"type\":\"shiki\",\"excludeLangs\":[\"math\"]},\"shikiConfig\":{\"langs\":[],\"langAlias\":{},\"theme\":\"github-dark\",\"themes\":{},\"wrap\":false,\"transformers\":[]},\"remarkPlugins\":[],\"rehypePlugins\":[],\"remarkRehype\":{},\"gfm\":true,\"smartypants\":true},\"security\":{\"checkOrigin\":true},\"env\":{\"schema\":{},\"validateSecrets\":false},\"experimental\":{\"clientPrerender\":false,\"contentIntellisense\":false,\"headingIdCompat\":false,\"preserveScriptOrder\":false,\"liveContentCollections\":false,\"csp\":false,\"rawEnvValues\":false},\"legacy\":{\"collections\":false},\"session\":{\"driver\":\"fs-lite\",\"options\":{\"base\":\"/var/home/user01/Projekte/forensic-pathways/node_modules/.astro/sessions\"}}}","knowledgebase",["Map",9,10,100,101,174,175,223,224,288,289,535,536],"misp",{"id":9,"data":11,"body":35,"filePath":36,"digest":37,"rendered":38,"legacyId":99},{"title":12,"tool_name":13,"description":14,"last_updated":15,"author":16,"difficulty":17,"categories":18,"tags":24,"sections":31,"review_status":34},"MISP - Plattform für Threat Intelligence Sharing","MISP","Das Rückgrat des modernen Threat-Intelligence-Sharings mit über 40.000 aktiven Instanzen weltweit.",["Date","2025-07-20T00:00:00.000Z"],"Claude 4 Sonnet","intermediate",[19,20,21,22,23],"incident-response","static-investigations","malware-analysis","network-forensics","cloud-forensics",[25,26,27,28,29,30],"web-based","threat-intelligence","api","correlation","ioc-sharing","automation",{"overview":32,"installation":32,"configuration":32,"usage_examples":32,"best_practices":32,"troubleshooting":32,"advanced_topics":33},true,false,"published","> **⚠️ Hinweis**: Dies ist ein vorläufiger, KI-generierter Knowledgebase-Eintrag. Wir freuen uns über Verbesserungen und Ergänzungen durch die Community!\n\n\n# Übersicht\n\n**MISP (Malware Information Sharing Platform & Threat Sharing)** ist eine freie Open-Source-Plattform zur strukturierten Erfassung, Speicherung, Analyse und gemeinsamen Nutzung von Cyber-Bedrohungsdaten. Mit über 40.000 Instanzen weltweit ist MISP der De-facto-Standard für den Austausch von Indicators of Compromise (IoCs) und Threat Intelligence zwischen CERTs, SOCs, Strafverfolgungsbehörden und anderen sicherheitsrelevanten Organisationen.\n\nDie föderierte Architektur ermöglicht einen kontrollierten, dezentralen Austausch von Informationen über vertrauenswürdige Partner hinweg. Durch Taxonomien, Tags und integrierte APIs ist eine automatische Anreicherung, Korrelation und Verarbeitung von Informationen in SIEMs, Firewalls oder Endpoint-Lösungen möglich.\n\n## Installation\n\n### Voraussetzungen\n\n- **Server-Betriebssystem:** Linux (empfohlen: Debian/Ubuntu)\n- **Abhängigkeiten:** MariaDB/MySQL, PHP, Apache/Nginx, Redis\n- **Ressourcen:** Mindestens 4 GB RAM, SSD empfohlen\n\n### Installationsschritte\n\n```bash\n# Beispiel für Debian/Ubuntu:\nsudo apt update && sudo apt install -y curl gnupg git python3 python3-pip redis-server mariadb-server apache2 php libapache2-mod-php\n\n# MISP klonen\ngit clone https://github.com/MISP/MISP.git /var/www/MISP\n\n# Setup-Skript nutzen\ncd /var/www/MISP && bash INSTALL/INSTALL.debian.sh\n````\n\nWeitere Details: [Offizielle Installationsanleitung](https://misp.github.io/MISP/INSTALL.debian/)\n\n## Konfiguration\n\n### Webserver\n\n* HTTPS aktivieren (Let's Encrypt oder Reverse Proxy)\n* PHP-Konfiguration anpassen (`upload_max_filesize`, `memory_limit`, `post_max_size`)\n\n### Benutzerrollen\n\n* Administrator, Org-Admin, Analyst etc.\n* Zugriffsbeschränkungen nach Organisation/Feed definierbar\n\n### Feeds und Galaxies\n\n* Aktivierung von Feeds (z. B. CIRCL, Abuse.ch, OpenCTI)\n* Nutzung von Galaxies zur Klassifizierung (APT-Gruppen, Malware-Familien)\n\n## Verwendungsbeispiele\n\n### Beispiel 1: Import von IoCs aus externem Feed\n\n1. Feed aktivieren unter **Administration → List Feeds**\n2. Feed synchronisieren\n3. Ereignisse durchsuchen, analysieren, ggf. mit eigenen Daten korrelieren\n\n### Beispiel 2: Automatisierte Anbindung an SIEM\n\n* REST-API-Token erstellen\n* API-Calls zur Abfrage neuer Events (z. B. mit Python, Logstash oder MISP Workbench)\n* Integration in Security-Systeme über JSON/STIX export\n\n## Best Practices\n\n* Regelmäßige Backups der Datenbank\n* Taxonomien konsistent verwenden\n* Nutzung der Sighting-Funktion zur Validierung von IoCs\n* Vertrauensstufen (TLP, PAP) korrekt setzen\n* Nicht nur konsumieren – auch teilen!\n\n## Troubleshooting\n\n### Problem: MISP-Feeds laden nicht\n\n**Lösung:**\n\n* Internetverbindung prüfen\n* Cronjobs aktiv?\n* Logs prüfen: `/var/www/MISP/app/tmp/logs/error.log`\n\n### Problem: API gibt 403 zurück\n\n**Lösung:**\n\n* Ist der API-Key korrekt und aktiv?\n* Rechte des Benutzers überprüfen\n* IP-Filter im MISP-Backend beachten\n\n### Problem: Hohe Datenbanklast\n\n**Lösung:**\n\n* Indizes optimieren\n* Redis aktivieren\n* Alte Events regelmäßig archivieren oder löschen\n\n## Weiterführende Themen\n\n* STIX2-Import/Export\n* Erweiterungen mit MISP Modules (z. B. für Virustotal, YARA)\n* Föderierte Netzwerke und Community-Portale\n* Integration mit OpenCTI oder TheHive\n\n---\n\n**Links:**\n\n* 🌐 [Offizielle Projektseite](https://misp-project.org/)\n* 📦 [CC24-MISP-Instanz](https://misp.cc24.dev)\n* 📊 [Status-Monitoring](https://status.mikoshi.de/api/badge/34/status)\n\nLizenz: **AGPL-3.0**","src/content/knowledgebase/misp.md","35930fa919a46964",{"html":39,"metadata":40},"\u003Cblockquote>\n\u003Cp>\u003Cstrong>⚠️ Hinweis\u003C/strong>: Dies ist ein vorläufiger, KI-generierter Knowledgebase-Eintrag. Wir freuen uns über Verbesserungen und Ergänzungen durch die Community!\u003C/p>\n\u003C/blockquote>\n\u003Ch1 id=\"übersicht\">Übersicht\u003C/h1>\n\u003Cp>\u003Cstrong>MISP (Malware Information Sharing Platform & Threat Sharing)\u003C/strong> ist eine freie Open-Source-Plattform zur strukturierten Erfassung, Speicherung, Analyse und gemeinsamen Nutzung von Cyber-Bedrohungsdaten. Mit über 40.000 Instanzen weltweit ist MISP der De-facto-Standard für den Austausch von Indicators of Compromise (IoCs) und Threat Intelligence zwischen CERTs, SOCs, Strafverfolgungsbehörden und anderen sicherheitsrelevanten Organisationen.\u003C/p>\n\u003Cp>Die föderierte Architektur ermöglicht einen kontrollierten, dezentralen Austausch von Informationen über vertrauenswürdige Partner hinweg. Durch Taxonomien, Tags und integrierte APIs ist eine automatische Anreicherung, Korrelation und Verarbeitung von Informationen in SIEMs, Firewalls oder Endpoint-Lösungen möglich.\u003C/p>\n\u003Ch2 id=\"installation\">Installation\u003C/h2>\n\u003Ch3 id=\"voraussetzungen\">Voraussetzungen\u003C/h3>\n\u003Cul>\n\u003Cli>\u003Cstrong>Server-Betriebssystem:\u003C/strong> Linux (empfohlen: Debian/Ubuntu)\u003C/li>\n\u003Cli>\u003Cstrong>Abhängigkeiten:\u003C/strong> MariaDB/MySQL, PHP, Apache/Nginx, Redis\u003C/li>\n\u003Cli>\u003Cstrong>Ressourcen:\u003C/strong> Mindestens 4 GB RAM, SSD empfohlen\u003C/li>\n\u003C/ul>\n\u003Ch3 id=\"installationsschritte\">Installationsschritte\u003C/h3>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"bash\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># Beispiel für Debian/Ubuntu:\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">sudo\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> apt\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> update\u003C/span>\u003Cspan style=\"color:#E1E4E8\"> && \u003C/span>\u003Cspan style=\"color:#B392F0\">sudo\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> apt\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> install\u003C/span>\u003Cspan style=\"color:#79B8FF\"> -y\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> curl\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> gnupg\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> git\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> python3\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> python3-pip\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> redis-server\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> mariadb-server\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> apache2\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> php\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> libapache2-mod-php\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># MISP klonen\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">git\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> clone\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> https://github.com/MISP/MISP.git\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> /var/www/MISP\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># Setup-Skript nutzen\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#79B8FF\">cd\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> /var/www/MISP\u003C/span>\u003Cspan style=\"color:#E1E4E8\"> && \u003C/span>\u003Cspan style=\"color:#B392F0\">bash\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> INSTALL/INSTALL.debian.sh\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Cp>Weitere Details: \u003Ca href=\"https://misp.github.io/MISP/INSTALL.debian/\">Offizielle Installationsanleitung\u003C/a>\u003C/p>\n\u003Ch2 id=\"konfiguration\">Konfiguration\u003C/h2>\n\u003Ch3 id=\"webserver\">Webserver\u003C/h3>\n\u003Cul>\n\u003Cli>HTTPS aktivieren (Let’s Encrypt oder Reverse Proxy)\u003C/li>\n\u003Cli>PHP-Konfiguration anpassen (\u003Ccode>upload_max_filesize\u003C/code>, \u003Ccode>memory_limit\u003C/code>, \u003Ccode>post_max_size\u003C/code>)\u003C/li>\n\u003C/ul>\n\u003Ch3 id=\"benutzerrollen\">Benutzerrollen\u003C/h3>\n\u003Cul>\n\u003Cli>Administrator, Org-Admin, Analyst etc.\u003C/li>\n\u003Cli>Zugriffsbeschränkungen nach Organisation/Feed definierbar\u003C/li>\n\u003C/ul>\n\u003Ch3 id=\"feeds-und-galaxies\">Feeds und Galaxies\u003C/h3>\n\u003Cul>\n\u003Cli>Aktivierung von Feeds (z. B. CIRCL, Abuse.ch, OpenCTI)\u003C/li>\n\u003Cli>Nutzung von Galaxies zur Klassifizierung (APT-Gruppen, Malware-Familien)\u003C/li>\n\u003C/ul>\n\u003Ch2 id=\"verwendungsbeispiele\">Verwendungsbeispiele\u003C/h2>\n\u003Ch3 id=\"beispiel-1-import-von-iocs-aus-externem-feed\">Beispiel 1: Import von IoCs aus externem Feed\u003C/h3>\n\u003Col>\n\u003Cli>Feed aktivieren unter \u003Cstrong>Administration → List Feeds\u003C/strong>\u003C/li>\n\u003Cli>Feed synchronisieren\u003C/li>\n\u003Cli>Ereignisse durchsuchen, analysieren, ggf. mit eigenen Daten korrelieren\u003C/li>\n\u003C/ol>\n\u003Ch3 id=\"beispiel-2-automatisierte-anbindung-an-siem\">Beispiel 2: Automatisierte Anbindung an SIEM\u003C/h3>\n\u003Cul>\n\u003Cli>REST-API-Token erstellen\u003C/li>\n\u003Cli>API-Calls zur Abfrage neuer Events (z. B. mit Python, Logstash oder MISP Workbench)\u003C/li>\n\u003Cli>Integration in Security-Systeme über JSON/STIX export\u003C/li>\n\u003C/ul>\n\u003Ch2 id=\"best-practices\">Best Practices\u003C/h2>\n\u003Cul>\n\u003Cli>Regelmäßige Backups der Datenbank\u003C/li>\n\u003Cli>Taxonomien konsistent verwenden\u003C/li>\n\u003Cli>Nutzung der Sighting-Funktion zur Validierung von IoCs\u003C/li>\n\u003Cli>Vertrauensstufen (TLP, PAP) korrekt setzen\u003C/li>\n\u003Cli>Nicht nur konsumieren – auch teilen!\u003C/li>\n\u003C/ul>\n\u003Ch2 id=\"troubleshooting\">Troubleshooting\u003C/h2>\n\u003Ch3 id=\"problem-misp-feeds-laden-nicht\">Problem: MISP-Feeds laden nicht\u003C/h3>\n\u003Cp>\u003Cstrong>Lösung:\u003C/strong>\u003C/p>\n\u003Cul>\n\u003Cli>Internetverbindung prüfen\u003C/li>\n\u003Cli>Cronjobs aktiv?\u003C/li>\n\u003Cli>Logs prüfen: \u003Ccode>/var/www/MISP/app/tmp/logs/error.log\u003C/code>\u003C/li>\n\u003C/ul>\n\u003Ch3 id=\"problem-api-gibt-403-zurück\">Problem: API gibt 403 zurück\u003C/h3>\n\u003Cp>\u003Cstrong>Lösung:\u003C/strong>\u003C/p>\n\u003Cul>\n\u003Cli>Ist der API-Key korrekt und aktiv?\u003C/li>\n\u003Cli>Rechte des Benutzers überprüfen\u003C/li>\n\u003Cli>IP-Filter im MISP-Backend beachten\u003C/li>\n\u003C/ul>\n\u003Ch3 id=\"problem-hohe-datenbanklast\">Problem: Hohe Datenbanklast\u003C/h3>\n\u003Cp>\u003Cstrong>Lösung:\u003C/strong>\u003C/p>\n\u003Cul>\n\u003Cli>Indizes optimieren\u003C/li>\n\u003Cli>Redis aktivieren\u003C/li>\n\u003Cli>Alte Events regelmäßig archivieren oder löschen\u003C/li>\n\u003C/ul>\n\u003Ch2 id=\"weiterführende-themen\">Weiterführende Themen\u003C/h2>\n\u003Cul>\n\u003Cli>STIX2-Import/Export\u003C/li>\n\u003Cli>Erweiterungen mit MISP Modules (z. B. für Virustotal, YARA)\u003C/li>\n\u003Cli>Föderierte Netzwerke und Community-Portale\u003C/li>\n\u003Cli>Integration mit OpenCTI oder TheHive\u003C/li>\n\u003C/ul>\n\u003Chr>\n\u003Cp>\u003Cstrong>Links:\u003C/strong>\u003C/p>\n\u003Cul>\n\u003Cli>🌐 \u003Ca href=\"https://misp-project.org/\">Offizielle Projektseite\u003C/a>\u003C/li>\n\u003Cli>📦 \u003Ca href=\"https://misp.cc24.dev\">CC24-MISP-Instanz\u003C/a>\u003C/li>\n\u003Cli>📊 \u003Ca href=\"https://status.mikoshi.de/api/badge/34/status\">Status-Monitoring\u003C/a>\u003C/li>\n\u003C/ul>\n\u003Cp>Lizenz: \u003Cstrong>AGPL-3.0\u003C/strong>\u003C/p>",{"headings":41,"localImagePaths":96,"remoteImagePaths":97,"frontmatter":11,"imagePaths":98},[42,46,50,54,57,60,63,66,69,72,75,78,81,84,87,90,93],{"depth":43,"slug":44,"text":45},1,"übersicht","Übersicht",{"depth":47,"slug":48,"text":49},2,"installation","Installation",{"depth":51,"slug":52,"text":53},3,"voraussetzungen","Voraussetzungen",{"depth":51,"slug":55,"text":56},"installationsschritte","Installationsschritte",{"depth":47,"slug":58,"text":59},"konfiguration","Konfiguration",{"depth":51,"slug":61,"text":62},"webserver","Webserver",{"depth":51,"slug":64,"text":65},"benutzerrollen","Benutzerrollen",{"depth":51,"slug":67,"text":68},"feeds-und-galaxies","Feeds und Galaxies",{"depth":47,"slug":70,"text":71},"verwendungsbeispiele","Verwendungsbeispiele",{"depth":51,"slug":73,"text":74},"beispiel-1-import-von-iocs-aus-externem-feed","Beispiel 1: Import von IoCs aus externem Feed",{"depth":51,"slug":76,"text":77},"beispiel-2-automatisierte-anbindung-an-siem","Beispiel 2: Automatisierte Anbindung an SIEM",{"depth":47,"slug":79,"text":80},"best-practices","Best Practices",{"depth":47,"slug":82,"text":83},"troubleshooting","Troubleshooting",{"depth":51,"slug":85,"text":86},"problem-misp-feeds-laden-nicht","Problem: MISP-Feeds laden nicht",{"depth":51,"slug":88,"text":89},"problem-api-gibt-403-zurück","Problem: API gibt 403 zurück",{"depth":51,"slug":91,"text":92},"problem-hohe-datenbanklast","Problem: Hohe Datenbanklast",{"depth":47,"slug":94,"text":95},"weiterführende-themen","Weiterführende Themen",[],[],[],"misp.md","kali-linux",{"id":100,"data":102,"body":117,"filePath":118,"digest":119,"rendered":120,"legacyId":173},{"title":103,"tool_name":104,"description":105,"last_updated":106,"author":16,"difficulty":17,"categories":107,"tags":110,"sections":116,"review_status":34},"Kali Linux - Die Hacker-Distribution für Forensik & Penetration Testing","Kali Linux","Leitfaden zur Installation, Nutzung und Best Practices für Kali Linux – die All-in-One-Plattform für Security-Profis.",["Date","2025-07-20T00:00:00.000Z"],[19,108,109],"forensics","penetration-testing",[111,112,109,113,114,115],"live-boot","tool-collection","forensics-suite","virtualization","arm-support",{"overview":32,"installation":32,"configuration":32,"usage_examples":32,"best_practices":32,"troubleshooting":32,"advanced_topics":32},"> **⚠️ Hinweis**: Dies ist ein vorläufiger, KI-generierter Knowledgebase-Eintrag. Wir freuen uns über Verbesserungen und Ergänzungen durch die Community!\n\n\n# Übersicht\n\nKali Linux ist eine auf Debian basierende Linux-Distribution, die speziell für Penetration Testing, digitale Forensik, Reverse Engineering und Incident Response entwickelt wurde. Mit über 600 vorinstallierten Tools ist sie ein unverzichtbares Werkzeug für Security-Experten, Ermittler und forensische Analysten. Die Live-Boot-Funktion erlaubt es, Systeme ohne Spuren zu hinterlassen zu analysieren – ideal für forensische Untersuchungen.\n\n## Installation\n\n### Option 1: Live-System (USB/DVD)\n\n1. ISO-Image von [kali.org](https://www.kali.org/get-kali/) herunterladen.\n2. Mit **Rufus** oder **balenaEtcher** auf einen USB-Stick schreiben.\n3. Vom USB-Stick booten (ggf. Boot-Reihenfolge im BIOS anpassen).\n4. Kali kann direkt ohne Installation im Live-Modus verwendet werden.\n\n### Option 2: Installation auf Festplatte\n\n1. ISO-Image booten und **Graphical Install** wählen.\n2. Schritt-für-Schritt durch den Installationsassistenten navigieren:\n   - Sprache, Zeitzone und Tastaturlayout auswählen\n   - Partitionierung konfigurieren (automatisch oder manuell)\n   - Benutzerkonten erstellen\n3. Nach Installation Neustart durchführen.\n\n### Option 3: Virtuelle Maschine (VM)\n\n- Offizielle VM-Images für VirtualBox und VMware von der [Kali-Website](https://www.kali.org/get-kali/#kali-virtual-machines)\n- Importieren, ggf. Netzwerkbrücke und Shared Folders aktivieren\n\n## Konfiguration\n\n### Netzwerkeinstellungen\n\n- Konfiguration über `nmtui` oder `/etc/network/interfaces`\n- VPN und Proxy-Integration über GUI oder Terminal\n\n### Updates & Paketquellen\n\n```bash\nsudo apt update && sudo apt full-upgrade\n````\n\n> Hinweis: `kali-rolling` ist die Standard-Distribution für kontinuierliche Updates.\n\n### Sprache & Lokalisierung\n\n```bash\nsudo dpkg-reconfigure locales\nsudo dpkg-reconfigure keyboard-configuration\n```\n\n## Verwendungsbeispiele\n\n### 1. Netzwerkscan mit Nmap\n\n```bash\nnmap -sS -T4 -A 192.168.1.0/24\n```\n\n### 2. Passwort-Cracking mit John the Ripper\n\n```bash\njohn --wordlist=/usr/share/wordlists/rockyou.txt hashes.txt\n```\n\n### 3. Forensik mit Autopsy\n\n```bash\nautopsy &\n```\n\n### 4. Android-Analyse mit MobSF (in Docker)\n\n```bash\ndocker pull opensecurity/mobile-security-framework-mobsf\ndocker run -it -p 8000:8000 mobsf\n```\n\n## Best Practices\n\n* Nutze immer **aktuelle Snapshots** oder VM-Clones vor gefährlichen Tests\n* Verwende separate Netzwerke (z. B. Host-only oder NAT) für Tests\n* Deaktiviere automatisches WLAN bei forensischen Analysen\n* Prüfe und aktualisiere regelmäßig Toolsets (`apt`, `git`, `pip`)\n* Halte deine ISO-Images versioniert für forensische Reproduzierbarkeit\n\n## Troubleshooting\n\n### Problem: Keine Internetverbindung nach Installation\n\n**Lösung:** Netzwerkadapter prüfen, ggf. mit `ifconfig` oder `ip a` überprüfen, DHCP aktivieren.\n\n### Problem: Tools fehlen nach Update\n\n**Lösung:** Tool-Gruppen wie `kali-linux-default` manuell nachinstallieren:\n\n```bash\nsudo apt install kali-linux-default\n```\n\n### Problem: „Permission Denied“ bei Tools\n\n**Lösung:** Root-Rechte nutzen oder mit `sudo` ausführen.\n\n## Weiterführende Themen\n\n* **Kustomisierung von Kali ISOs** mit `live-build`\n* **NetHunter**: Kali für mobile Geräte (Android)\n* **Kali Purple**: Defensive Security Suite\n* Integration mit **Cloud-Infrastrukturen** via WSL oder Azure\n\n---\n\n**Links & Ressourcen:**\n\n* Offizielle Website: [https://kali.org](https://kali.org/)\n* Dokumentation: [https://docs.kali.org/](https://docs.kali.org/)\n* GitLab Repo: [https://gitlab.com/kalilinux](https://gitlab.com/kalilinux)\n* Discord-Community: [https://discord.com/invite/kali-linux](https://discord.com/invite/kali-linux)","src/content/knowledgebase/kali-linux.md","09243ebc79d75dbc",{"html":121,"metadata":122},"\u003Cblockquote>\n\u003Cp>\u003Cstrong>⚠️ Hinweis\u003C/strong>: Dies ist ein vorläufiger, KI-generierter Knowledgebase-Eintrag. Wir freuen uns über Verbesserungen und Ergänzungen durch die Community!\u003C/p>\n\u003C/blockquote>\n\u003Ch1 id=\"übersicht\">Übersicht\u003C/h1>\n\u003Cp>Kali Linux ist eine auf Debian basierende Linux-Distribution, die speziell für Penetration Testing, digitale Forensik, Reverse Engineering und Incident Response entwickelt wurde. Mit über 600 vorinstallierten Tools ist sie ein unverzichtbares Werkzeug für Security-Experten, Ermittler und forensische Analysten. Die Live-Boot-Funktion erlaubt es, Systeme ohne Spuren zu hinterlassen zu analysieren – ideal für forensische Untersuchungen.\u003C/p>\n\u003Ch2 id=\"installation\">Installation\u003C/h2>\n\u003Ch3 id=\"option-1-live-system-usbdvd\">Option 1: Live-System (USB/DVD)\u003C/h3>\n\u003Col>\n\u003Cli>ISO-Image von \u003Ca href=\"https://www.kali.org/get-kali/\">kali.org\u003C/a> herunterladen.\u003C/li>\n\u003Cli>Mit \u003Cstrong>Rufus\u003C/strong> oder \u003Cstrong>balenaEtcher\u003C/strong> auf einen USB-Stick schreiben.\u003C/li>\n\u003Cli>Vom USB-Stick booten (ggf. Boot-Reihenfolge im BIOS anpassen).\u003C/li>\n\u003Cli>Kali kann direkt ohne Installation im Live-Modus verwendet werden.\u003C/li>\n\u003C/ol>\n\u003Ch3 id=\"option-2-installation-auf-festplatte\">Option 2: Installation auf Festplatte\u003C/h3>\n\u003Col>\n\u003Cli>ISO-Image booten und \u003Cstrong>Graphical Install\u003C/strong> wählen.\u003C/li>\n\u003Cli>Schritt-für-Schritt durch den Installationsassistenten navigieren:\n\u003Cul>\n\u003Cli>Sprache, Zeitzone und Tastaturlayout auswählen\u003C/li>\n\u003Cli>Partitionierung konfigurieren (automatisch oder manuell)\u003C/li>\n\u003Cli>Benutzerkonten erstellen\u003C/li>\n\u003C/ul>\n\u003C/li>\n\u003Cli>Nach Installation Neustart durchführen.\u003C/li>\n\u003C/ol>\n\u003Ch3 id=\"option-3-virtuelle-maschine-vm\">Option 3: Virtuelle Maschine (VM)\u003C/h3>\n\u003Cul>\n\u003Cli>Offizielle VM-Images für VirtualBox und VMware von der \u003Ca href=\"https://www.kali.org/get-kali/#kali-virtual-machines\">Kali-Website\u003C/a>\u003C/li>\n\u003Cli>Importieren, ggf. Netzwerkbrücke und Shared Folders aktivieren\u003C/li>\n\u003C/ul>\n\u003Ch2 id=\"konfiguration\">Konfiguration\u003C/h2>\n\u003Ch3 id=\"netzwerkeinstellungen\">Netzwerkeinstellungen\u003C/h3>\n\u003Cul>\n\u003Cli>Konfiguration über \u003Ccode>nmtui\u003C/code> oder \u003Ccode>/etc/network/interfaces\u003C/code>\u003C/li>\n\u003Cli>VPN und Proxy-Integration über GUI oder Terminal\u003C/li>\n\u003C/ul>\n\u003Ch3 id=\"updates--paketquellen\">Updates & Paketquellen\u003C/h3>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"bash\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">sudo\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> apt\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> update\u003C/span>\u003Cspan style=\"color:#E1E4E8\"> && \u003C/span>\u003Cspan style=\"color:#B392F0\">sudo\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> apt\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> full-upgrade\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Cblockquote>\n\u003Cp>Hinweis: \u003Ccode>kali-rolling\u003C/code> ist die Standard-Distribution für kontinuierliche Updates.\u003C/p>\n\u003C/blockquote>\n\u003Ch3 id=\"sprache--lokalisierung\">Sprache & Lokalisierung\u003C/h3>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"bash\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">sudo\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> dpkg-reconfigure\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> locales\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">sudo\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> dpkg-reconfigure\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> keyboard-configuration\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Ch2 id=\"verwendungsbeispiele\">Verwendungsbeispiele\u003C/h2>\n\u003Ch3 id=\"1-netzwerkscan-mit-nmap\">1. Netzwerkscan mit Nmap\u003C/h3>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"bash\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">nmap\u003C/span>\u003Cspan style=\"color:#79B8FF\"> -sS\u003C/span>\u003Cspan style=\"color:#79B8FF\"> -T4\u003C/span>\u003Cspan style=\"color:#79B8FF\"> -A\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> 192.168.1.0/24\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Ch3 id=\"2-passwort-cracking-mit-john-the-ripper\">2. Passwort-Cracking mit John the Ripper\u003C/h3>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"bash\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">john\u003C/span>\u003Cspan style=\"color:#79B8FF\"> --wordlist=/usr/share/wordlists/rockyou.txt\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> hashes.txt\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Ch3 id=\"3-forensik-mit-autopsy\">3. Forensik mit Autopsy\u003C/h3>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"bash\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">autopsy\u003C/span>\u003Cspan style=\"color:#E1E4E8\"> &\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Ch3 id=\"4-android-analyse-mit-mobsf-in-docker\">4. Android-Analyse mit MobSF (in Docker)\u003C/h3>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"bash\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">docker\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> pull\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> opensecurity/mobile-security-framework-mobsf\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">docker\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> run\u003C/span>\u003Cspan style=\"color:#79B8FF\"> -it\u003C/span>\u003Cspan style=\"color:#79B8FF\"> -p\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> 8000:8000\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> mobsf\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Ch2 id=\"best-practices\">Best Practices\u003C/h2>\n\u003Cul>\n\u003Cli>Nutze immer \u003Cstrong>aktuelle Snapshots\u003C/strong> oder VM-Clones vor gefährlichen Tests\u003C/li>\n\u003Cli>Verwende separate Netzwerke (z. B. Host-only oder NAT) für Tests\u003C/li>\n\u003Cli>Deaktiviere automatisches WLAN bei forensischen Analysen\u003C/li>\n\u003Cli>Prüfe und aktualisiere regelmäßig Toolsets (\u003Ccode>apt\u003C/code>, \u003Ccode>git\u003C/code>, \u003Ccode>pip\u003C/code>)\u003C/li>\n\u003Cli>Halte deine ISO-Images versioniert für forensische Reproduzierbarkeit\u003C/li>\n\u003C/ul>\n\u003Ch2 id=\"troubleshooting\">Troubleshooting\u003C/h2>\n\u003Ch3 id=\"problem-keine-internetverbindung-nach-installation\">Problem: Keine Internetverbindung nach Installation\u003C/h3>\n\u003Cp>\u003Cstrong>Lösung:\u003C/strong> Netzwerkadapter prüfen, ggf. mit \u003Ccode>ifconfig\u003C/code> oder \u003Ccode>ip a\u003C/code> überprüfen, DHCP aktivieren.\u003C/p>\n\u003Ch3 id=\"problem-tools-fehlen-nach-update\">Problem: Tools fehlen nach Update\u003C/h3>\n\u003Cp>\u003Cstrong>Lösung:\u003C/strong> Tool-Gruppen wie \u003Ccode>kali-linux-default\u003C/code> manuell nachinstallieren:\u003C/p>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"bash\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">sudo\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> apt\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> install\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> kali-linux-default\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Ch3 id=\"problem-permission-denied-bei-tools\">Problem: „Permission Denied“ bei Tools\u003C/h3>\n\u003Cp>\u003Cstrong>Lösung:\u003C/strong> Root-Rechte nutzen oder mit \u003Ccode>sudo\u003C/code> ausführen.\u003C/p>\n\u003Ch2 id=\"weiterführende-themen\">Weiterführende Themen\u003C/h2>\n\u003Cul>\n\u003Cli>\u003Cstrong>Kustomisierung von Kali ISOs\u003C/strong> mit \u003Ccode>live-build\u003C/code>\u003C/li>\n\u003Cli>\u003Cstrong>NetHunter\u003C/strong>: Kali für mobile Geräte (Android)\u003C/li>\n\u003Cli>\u003Cstrong>Kali Purple\u003C/strong>: Defensive Security Suite\u003C/li>\n\u003Cli>Integration mit \u003Cstrong>Cloud-Infrastrukturen\u003C/strong> via WSL oder Azure\u003C/li>\n\u003C/ul>\n\u003Chr>\n\u003Cp>\u003Cstrong>Links & Ressourcen:\u003C/strong>\u003C/p>\n\u003Cul>\n\u003Cli>Offizielle Website: \u003Ca href=\"https://kali.org/\">https://kali.org\u003C/a>\u003C/li>\n\u003Cli>Dokumentation: \u003Ca href=\"https://docs.kali.org/\">https://docs.kali.org/\u003C/a>\u003C/li>\n\u003Cli>GitLab Repo: \u003Ca href=\"https://gitlab.com/kalilinux\">https://gitlab.com/kalilinux\u003C/a>\u003C/li>\n\u003Cli>Discord-Community: \u003Ca href=\"https://discord.com/invite/kali-linux\">https://discord.com/invite/kali-linux\u003C/a>\u003C/li>\n\u003C/ul>",{"headings":123,"localImagePaths":170,"remoteImagePaths":171,"frontmatter":102,"imagePaths":172},[124,125,126,129,132,135,136,139,142,145,146,149,152,155,158,159,160,163,166,169],{"depth":43,"slug":44,"text":45},{"depth":47,"slug":48,"text":49},{"depth":51,"slug":127,"text":128},"option-1-live-system-usbdvd","Option 1: Live-System (USB/DVD)",{"depth":51,"slug":130,"text":131},"option-2-installation-auf-festplatte","Option 2: Installation auf Festplatte",{"depth":51,"slug":133,"text":134},"option-3-virtuelle-maschine-vm","Option 3: Virtuelle Maschine (VM)",{"depth":47,"slug":58,"text":59},{"depth":51,"slug":137,"text":138},"netzwerkeinstellungen","Netzwerkeinstellungen",{"depth":51,"slug":140,"text":141},"updates--paketquellen","Updates & Paketquellen",{"depth":51,"slug":143,"text":144},"sprache--lokalisierung","Sprache & Lokalisierung",{"depth":47,"slug":70,"text":71},{"depth":51,"slug":147,"text":148},"1-netzwerkscan-mit-nmap","1. Netzwerkscan mit Nmap",{"depth":51,"slug":150,"text":151},"2-passwort-cracking-mit-john-the-ripper","2. Passwort-Cracking mit John the Ripper",{"depth":51,"slug":153,"text":154},"3-forensik-mit-autopsy","3. Forensik mit Autopsy",{"depth":51,"slug":156,"text":157},"4-android-analyse-mit-mobsf-in-docker","4. Android-Analyse mit MobSF (in Docker)",{"depth":47,"slug":79,"text":80},{"depth":47,"slug":82,"text":83},{"depth":51,"slug":161,"text":162},"problem-keine-internetverbindung-nach-installation","Problem: Keine Internetverbindung nach Installation",{"depth":51,"slug":164,"text":165},"problem-tools-fehlen-nach-update","Problem: Tools fehlen nach Update",{"depth":51,"slug":167,"text":168},"problem-permission-denied-bei-tools","Problem: „Permission Denied“ bei Tools",{"depth":47,"slug":94,"text":95},[],[],[],"kali-linux.md","regular-expressions-regex",{"id":174,"data":176,"body":190,"filePath":191,"digest":192,"rendered":193,"legacyId":222},{"title":177,"tool_name":178,"description":179,"last_updated":180,"author":16,"difficulty":17,"categories":181,"tags":183,"sections":189,"review_status":34},"Regular Expressions (Regex) – Musterbasierte Textanalyse","Regular Expressions (Regex)","Pattern matching language für Suche, Extraktion und Manipulation von Text in forensischen Analysen.",["Date","2025-07-20T00:00:00.000Z"],[19,21,22,182],"fraud-investigation",[184,185,186,187,188],"pattern-matching","text-processing","log-analysis","string-manipulation","search-algorithms",{"overview":32,"installation":33,"configuration":33,"usage_examples":32,"best_practices":32,"troubleshooting":33,"advanced_topics":32},"> **⚠️ Hinweis**: Dies ist ein vorläufiger, KI-generierter Knowledgebase-Eintrag. Wir freuen uns über Verbesserungen und Ergänzungen durch die Community!\n\n\n# Übersicht\n\n**Regular Expressions (Regex)** sind ein leistungsfähiges Werkzeug zur Erkennung, Extraktion und Transformation von Zeichenfolgen anhand vordefinierter Muster. In der digitalen Forensik sind Regex-Ausdrücke unverzichtbar: Sie helfen beim Auffinden von IP-Adressen, Hash-Werten, Dateipfaden, Malware-Signaturen oder Kreditkartennummern in großen Mengen unstrukturierter Daten wie Logdateien, Netzwerktraces oder Memory Dumps.\n\nRegex ist nicht auf eine bestimmte Plattform oder Software beschränkt – es wird in nahezu allen gängigen Programmiersprachen, Texteditoren und forensischen Tools unterstützt.\n\n## Verwendungsbeispiele\n\n### 1. IP-Adressen extrahieren\n\n```regex\n\\b(?:\\d{1,3}\\.){3}\\d{1,3}\\b\n````\n\nVerwendung:\n\n* Finden von IP-Adressen in Firewall-Logs oder Packet Captures.\n* Beispiel-Zeile:\n\n  ```\n  Connection from 192.168.1.101 to port 443 established\n  ```\n\n### 2. E-Mail-Adressen identifizieren\n\n```regex\n[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\\.[a-zA-Z]{2,}\n```\n\nVerwendung:\n\n* Erkennung von kompromittierten Accounts in Phishing-E-Mails.\n* Analyse von Useraktivitäten oder Kommunikationsverläufen.\n\n### 3. Hash-Werte erkennen (z. B. SHA-256)\n\n```regex\n\\b[A-Fa-f0-9]{64}\\b\n```\n\nVerwendung:\n\n* Extraktion von Malware-Hashes aus Memory Dumps oder YARA-Logs.\n\n### 4. Zeitstempel in Logdateien extrahieren\n\n```regex\n\\d{4}-\\d{2}-\\d{2}[ T]\\d{2}:\\d{2}:\\d{2}\n```\n\nVerwendung:\n\n* Zeitsensitive Korrelationsanalysen (z. B. bei Intrusion Detection oder Timeline-Rekonstruktionen).\n\n## Best Practices\n\n* **Regex testen**: Nutze Plattformen wie [regexr.com](https://regexr.com/) oder [regex101.com](https://regex101.com/) zur Validierung.\n* **Performance beachten**: Komplexe Ausdrücke können ineffizient sein und Systeme verlangsamen – verwende Lazy Quantifiers (`*?`, `+?`) bei Bedarf.\n* **Escape-Zeichen korrekt anwenden**: Spezielle Zeichen wie `.` oder `\\` müssen bei Bedarf mit `\\\\` oder `\\.` maskiert werden.\n* **Portabilität prüfen**: Unterschiedliche Regex-Engines (z. B. Python `re`, PCRE, JavaScript) interpretieren manche Syntax leicht unterschiedlich.\n* **Lesbarkeit fördern**: Verwende benannte Gruppen (`(?P\u003Cname>...)`) und Kommentare (`(?x)`), um reguläre Ausdrücke besser wartbar zu machen.\n\n## Weiterführende Themen\n\n### Lookaheads und Lookbehinds\n\nMit **Lookaheads** (`(?=...)`) und **Lookbehinds** (`(?\u003C=...)`) können Bedingungen formuliert werden, ohne dass der Text Teil des Matchs wird.\n\nBeispiel: Alle `.exe`-Dateinamen **ohne** das Wort `safe` davor matchen:\n\n```regex\n(?\u003C!safe\\s)[\\w-]+\\.exe\n```\n\n### Regex in Forensik-Tools\n\n* **YARA**: Unterstützt Regex zur Erstellung von Malware-Signaturen.\n* **Wireshark**: Filtert Payloads anhand von Regex-ähnlicher Syntax.\n* **Splunk & ELK**: Verwenden Regex für Logparsing und Visualisierung.\n* **Volatility Plugins**: Extrahieren Artefakte mit Regex-basierten Scans.\n\n---\n\n> 🔤 **Regex ist ein universelles Werkzeug für Analysten, Ermittler und Entwickler, um versteckte Informationen schnell und flexibel aufzuspüren.**\n>\n> Nutze es überall dort, wo Textdaten eine Rolle spielen.","src/content/knowledgebase/regular-expressions-regex.md","247bcf48ebdc9ba0",{"html":194,"metadata":195},"\u003Cblockquote>\n\u003Cp>\u003Cstrong>⚠️ Hinweis\u003C/strong>: Dies ist ein vorläufiger, KI-generierter Knowledgebase-Eintrag. Wir freuen uns über Verbesserungen und Ergänzungen durch die Community!\u003C/p>\n\u003C/blockquote>\n\u003Ch1 id=\"übersicht\">Übersicht\u003C/h1>\n\u003Cp>\u003Cstrong>Regular Expressions (Regex)\u003C/strong> sind ein leistungsfähiges Werkzeug zur Erkennung, Extraktion und Transformation von Zeichenfolgen anhand vordefinierter Muster. In der digitalen Forensik sind Regex-Ausdrücke unverzichtbar: Sie helfen beim Auffinden von IP-Adressen, Hash-Werten, Dateipfaden, Malware-Signaturen oder Kreditkartennummern in großen Mengen unstrukturierter Daten wie Logdateien, Netzwerktraces oder Memory Dumps.\u003C/p>\n\u003Cp>Regex ist nicht auf eine bestimmte Plattform oder Software beschränkt – es wird in nahezu allen gängigen Programmiersprachen, Texteditoren und forensischen Tools unterstützt.\u003C/p>\n\u003Ch2 id=\"verwendungsbeispiele\">Verwendungsbeispiele\u003C/h2>\n\u003Ch3 id=\"1-ip-adressen-extrahieren\">1. IP-Adressen extrahieren\u003C/h3>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"regex\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#79B8FF\">\\b(?:\\d\u003C/span>\u003Cspan style=\"color:#F97583\">{1,3}\u003C/span>\u003Cspan style=\"color:#79B8FF\">\\.)\u003C/span>\u003Cspan style=\"color:#F97583\">{3}\u003C/span>\u003Cspan style=\"color:#79B8FF\">\\d\u003C/span>\u003Cspan style=\"color:#F97583\">{1,3}\u003C/span>\u003Cspan style=\"color:#79B8FF\">\\b\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Cp>Verwendung:\u003C/p>\n\u003Cul>\n\u003Cli>\n\u003Cp>Finden von IP-Adressen in Firewall-Logs oder Packet Captures.\u003C/p>\n\u003C/li>\n\u003Cli>\n\u003Cp>Beispiel-Zeile:\u003C/p>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"plaintext\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan>Connection from 192.168.1.101 to port 443 established\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003C/li>\n\u003C/ul>\n\u003Ch3 id=\"2-e-mail-adressen-identifizieren\">2. E-Mail-Adressen identifizieren\u003C/h3>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"regex\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#79B8FF\">[a-zA-Z0-9._%+-]\u003C/span>\u003Cspan style=\"color:#F97583\">+\u003C/span>\u003Cspan style=\"color:#DBEDFF\">@\u003C/span>\u003Cspan style=\"color:#79B8FF\">[a-zA-Z0-9.-]\u003C/span>\u003Cspan style=\"color:#F97583\">+\u003C/span>\u003Cspan style=\"color:#79B8FF\">\\.[a-zA-Z]\u003C/span>\u003Cspan style=\"color:#F97583\">{2,}\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Cp>Verwendung:\u003C/p>\n\u003Cul>\n\u003Cli>Erkennung von kompromittierten Accounts in Phishing-E-Mails.\u003C/li>\n\u003Cli>Analyse von Useraktivitäten oder Kommunikationsverläufen.\u003C/li>\n\u003C/ul>\n\u003Ch3 id=\"3-hash-werte-erkennen-zb-sha-256\">3. Hash-Werte erkennen (z. B. SHA-256)\u003C/h3>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"regex\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#79B8FF\">\\b[A-Fa-f0-9]\u003C/span>\u003Cspan style=\"color:#F97583\">{64}\u003C/span>\u003Cspan style=\"color:#79B8FF\">\\b\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Cp>Verwendung:\u003C/p>\n\u003Cul>\n\u003Cli>Extraktion von Malware-Hashes aus Memory Dumps oder YARA-Logs.\u003C/li>\n\u003C/ul>\n\u003Ch3 id=\"4-zeitstempel-in-logdateien-extrahieren\">4. Zeitstempel in Logdateien extrahieren\u003C/h3>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"regex\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#79B8FF\">\\d\u003C/span>\u003Cspan style=\"color:#F97583\">{4}\u003C/span>\u003Cspan style=\"color:#DBEDFF\">-\u003C/span>\u003Cspan style=\"color:#79B8FF\">\\d\u003C/span>\u003Cspan style=\"color:#F97583\">{2}\u003C/span>\u003Cspan style=\"color:#DBEDFF\">-\u003C/span>\u003Cspan style=\"color:#79B8FF\">\\d\u003C/span>\u003Cspan style=\"color:#F97583\">{2}\u003C/span>\u003Cspan style=\"color:#79B8FF\">[ T]\\d\u003C/span>\u003Cspan style=\"color:#F97583\">{2}\u003C/span>\u003Cspan style=\"color:#DBEDFF\">:\u003C/span>\u003Cspan style=\"color:#79B8FF\">\\d\u003C/span>\u003Cspan style=\"color:#F97583\">{2}\u003C/span>\u003Cspan style=\"color:#DBEDFF\">:\u003C/span>\u003Cspan style=\"color:#79B8FF\">\\d\u003C/span>\u003Cspan style=\"color:#F97583\">{2}\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Cp>Verwendung:\u003C/p>\n\u003Cul>\n\u003Cli>Zeitsensitive Korrelationsanalysen (z. B. bei Intrusion Detection oder Timeline-Rekonstruktionen).\u003C/li>\n\u003C/ul>\n\u003Ch2 id=\"best-practices\">Best Practices\u003C/h2>\n\u003Cul>\n\u003Cli>\u003Cstrong>Regex testen\u003C/strong>: Nutze Plattformen wie \u003Ca href=\"https://regexr.com/\">regexr.com\u003C/a> oder \u003Ca href=\"https://regex101.com/\">regex101.com\u003C/a> zur Validierung.\u003C/li>\n\u003Cli>\u003Cstrong>Performance beachten\u003C/strong>: Komplexe Ausdrücke können ineffizient sein und Systeme verlangsamen – verwende Lazy Quantifiers (\u003Ccode>*?\u003C/code>, \u003Ccode>+?\u003C/code>) bei Bedarf.\u003C/li>\n\u003Cli>\u003Cstrong>Escape-Zeichen korrekt anwenden\u003C/strong>: Spezielle Zeichen wie \u003Ccode>.\u003C/code> oder \u003Ccode>\\\u003C/code> müssen bei Bedarf mit \u003Ccode>\\\\\u003C/code> oder \u003Ccode>\\.\u003C/code> maskiert werden.\u003C/li>\n\u003Cli>\u003Cstrong>Portabilität prüfen\u003C/strong>: Unterschiedliche Regex-Engines (z. B. Python \u003Ccode>re\u003C/code>, PCRE, JavaScript) interpretieren manche Syntax leicht unterschiedlich.\u003C/li>\n\u003Cli>\u003Cstrong>Lesbarkeit fördern\u003C/strong>: Verwende benannte Gruppen (\u003Ccode>(?P<name>...)\u003C/code>) und Kommentare (\u003Ccode>(?x)\u003C/code>), um reguläre Ausdrücke besser wartbar zu machen.\u003C/li>\n\u003C/ul>\n\u003Ch2 id=\"weiterführende-themen\">Weiterführende Themen\u003C/h2>\n\u003Ch3 id=\"lookaheads-und-lookbehinds\">Lookaheads und Lookbehinds\u003C/h3>\n\u003Cp>Mit \u003Cstrong>Lookaheads\u003C/strong> (\u003Ccode>(?=...)\u003C/code>) und \u003Cstrong>Lookbehinds\u003C/strong> (\u003Ccode>(?<=...)\u003C/code>) können Bedingungen formuliert werden, ohne dass der Text Teil des Matchs wird.\u003C/p>\n\u003Cp>Beispiel: Alle \u003Ccode>.exe\u003C/code>-Dateinamen \u003Cstrong>ohne\u003C/strong> das Wort \u003Ccode>safe\u003C/code> davor matchen:\u003C/p>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"regex\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#F97583\">(?<!\u003C/span>\u003Cspan style=\"color:#DBEDFF\">safe\u003C/span>\u003Cspan style=\"color:#79B8FF\">\\s\u003C/span>\u003Cspan style=\"color:#F97583\">)\u003C/span>\u003Cspan style=\"color:#79B8FF\">[\\w-]\u003C/span>\u003Cspan style=\"color:#F97583\">+\u003C/span>\u003Cspan style=\"color:#79B8FF\">\\.\u003C/span>\u003Cspan style=\"color:#DBEDFF\">exe\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Ch3 id=\"regex-in-forensik-tools\">Regex in Forensik-Tools\u003C/h3>\n\u003Cul>\n\u003Cli>\u003Cstrong>YARA\u003C/strong>: Unterstützt Regex zur Erstellung von Malware-Signaturen.\u003C/li>\n\u003Cli>\u003Cstrong>Wireshark\u003C/strong>: Filtert Payloads anhand von Regex-ähnlicher Syntax.\u003C/li>\n\u003Cli>\u003Cstrong>Splunk & ELK\u003C/strong>: Verwenden Regex für Logparsing und Visualisierung.\u003C/li>\n\u003Cli>\u003Cstrong>Volatility Plugins\u003C/strong>: Extrahieren Artefakte mit Regex-basierten Scans.\u003C/li>\n\u003C/ul>\n\u003Chr>\n\u003Cblockquote>\n\u003Cp>🔤 \u003Cstrong>Regex ist ein universelles Werkzeug für Analysten, Ermittler und Entwickler, um versteckte Informationen schnell und flexibel aufzuspüren.\u003C/strong>\u003C/p>\n\u003Cp>Nutze es überall dort, wo Textdaten eine Rolle spielen.\u003C/p>\n\u003C/blockquote>",{"headings":196,"localImagePaths":219,"remoteImagePaths":220,"frontmatter":176,"imagePaths":221},[197,198,199,202,205,208,211,212,213,216],{"depth":43,"slug":44,"text":45},{"depth":47,"slug":70,"text":71},{"depth":51,"slug":200,"text":201},"1-ip-adressen-extrahieren","1. IP-Adressen extrahieren",{"depth":51,"slug":203,"text":204},"2-e-mail-adressen-identifizieren","2. E-Mail-Adressen identifizieren",{"depth":51,"slug":206,"text":207},"3-hash-werte-erkennen-zb-sha-256","3. Hash-Werte erkennen (z. B. SHA-256)",{"depth":51,"slug":209,"text":210},"4-zeitstempel-in-logdateien-extrahieren","4. Zeitstempel in Logdateien extrahieren",{"depth":47,"slug":79,"text":80},{"depth":47,"slug":94,"text":95},{"depth":51,"slug":214,"text":215},"lookaheads-und-lookbehinds","Lookaheads und Lookbehinds",{"depth":51,"slug":217,"text":218},"regex-in-forensik-tools","Regex in Forensik-Tools",[],[],[],"regular-expressions-regex.md","velociraptor",{"id":223,"data":225,"body":239,"filePath":240,"digest":241,"rendered":242,"legacyId":287},{"title":226,"tool_name":227,"description":228,"last_updated":229,"author":16,"difficulty":230,"categories":231,"tags":232,"sections":238,"review_status":34},"Velociraptor – Skalierbare Endpoint-Forensik mit VQL","Velociraptor","Detaillierte Anleitung und Best Practices für Velociraptor – Remote-Forensik der nächsten Generation",["Date","2025-07-20T00:00:00.000Z"],"advanced",[19,21,22],[25,233,234,235,236,237],"endpoint-monitoring","artifact-extraction","scripting","live-forensics","hunting",{"overview":32,"installation":32,"configuration":32,"usage_examples":32,"best_practices":32,"troubleshooting":32,"advanced_topics":32},"> **⚠️ Hinweis**: Dies ist ein vorläufiger, KI-generierter Knowledgebase-Eintrag. Wir freuen uns über Verbesserungen und Ergänzungen durch die Community!\n\n\n# Übersicht\n\nVelociraptor ist ein Open-Source-Tool zur Endpoint-Forensik mit Fokus auf Skalierbarkeit, Präzision und Geschwindigkeit. Es ermöglicht die zielgerichtete Erfassung und Analyse digitaler Artefakte über eine eigene Query Language – VQL (Velociraptor Query Language). Die Architektur erlaubt remote Zugriff auf tausende Endpoints gleichzeitig, ohne dass vollständige Disk-Images erforderlich sind.\n\n## Hauptmerkmale\n\n- 🌐 Web-basierte Benutzeroberfläche\n- 💡 VQL – mächtige, SQL-ähnliche Abfragesprache\n- 🚀 Hochskalierbare Hunt-Funktionalität\n- 🔍 Artefaktbasierte Sammlung (ohne Full-Image)\n- 🖥️ Plattformunterstützung für Windows, macOS, Linux\n- 📦 Apache 2.0 Lizenz – Open Source\n\nWeitere Infos: [velociraptor.app](https://www.velociraptor.app/)  \nProjektspiegel: [raptor.cc24.dev](https://raptor.cc24.dev)  \nStatus: ![Status](https://status.mikoshi.de/api/badge/33/status)\n\n---\n\n## Installation\n\n### Voraussetzungen\n\n- Python ≥ 3.9\n- Adminrechte auf dem System\n- Firewall-Freigaben für Webport (Standard: 8000)\n\n### Installation unter Linux/macOS\n\n```bash\nwget https://github.com/Velocidex/velociraptor/releases/latest/download/velociraptor\nchmod +x velociraptor\nsudo mv velociraptor /usr/local/bin/\n````\n\n### Installation unter Windows\n\n1. Download der `.exe` von der [Release-Seite](https://github.com/Velocidex/velociraptor/releases)\n2. Ausführung in PowerShell mit Adminrechten:\n\n   ```powershell\n   .\\velociraptor.exe config generate > server.config.yaml\n   ```\n\n---\n\n## Konfiguration\n\n### Server Setup\n\n1. Generiere die Konfigurationsdatei:\n\n   ```bash\n   velociraptor config generate > server.config.yaml\n   ```\n2. Starte den Server:\n\n   ```bash\n   velociraptor --config server.config.yaml frontend\n   ```\n3. Zugriff über Browser via `https://\u003Chostname>:8000`\n\n### Client Deployment\n\n* MSI/EXE für Windows, oder `deb/rpm` für Linux\n* Unterstützt automatische Registrierung am Server\n* Deployment über GPO, Puppet, Ansible etc. möglich\n\n---\n\n## Verwendungsbeispiele\n\n### 1. Live-Memory-Artefakte sammeln\n\n```vql\nSELECT * FROM Artifact.MemoryInfo()\n```\n\n### 2. Hunt starten auf verdächtige Prozesse\n\n```vql\nSELECT * FROM pslist()\nWHERE Name =~ \"mimikatz|cobaltstrike\"\n```\n\n### 3. Dateiinhalt extrahieren\n\n```vql\nSELECT * FROM glob(globs=\"C:\\\\Users\\\\*\\\\AppData\\\\*.dat\")\n```\n\n---\n\n## Best Practices\n\n* Erstelle eigene Artefakte für unternehmensspezifische Bedrohungsmodelle\n* Verwende \"Notebook\"-Funktion für strukturierte Analysen\n* Nutze \"Labels\", um Endpoints zu organisieren (z. B. `location:Berlin`)\n* Kombiniere Velociraptor mit SIEM/EDR-Systemen über REST API\n\n---\n\n## Troubleshooting\n\n### Problem: Keine Verbindung vom Client zum Server\n\n**Lösung:**\n\n* Ports freigegeben? (Default: 8000/tcp)\n* TLS-Zertifikate korrekt generiert?\n* `server.config.yaml` auf korrekte `public_ip` prüfen\n\n### Problem: Hunt hängt in Warteschleife\n\n**Lösung:**\n\n* Genügend Worker-Prozesse aktiv?\n* Endpoint online?\n* `log_level` auf `debug` setzen und Log analysieren\n\n---\n\n## Weiterführende Themen\n\n* Eigene Artefakte schreiben mit VQL\n* Integration mit ELK Stack\n* Automatisiertes Incident Response Playbook\n* Velociraptor als IR-as-a-Service einsetzen\n\n---\n\n🧠 **Tipp:** Die Lernkurve bei VQL ist steil – aber mit hohem ROI. Testumgebung aufsetzen und mit Community-Artefakten starten.\n\n📚 Weitere Ressourcen:\n\n* [Offizielle Doku](https://docs.velociraptor.app/)\n* [YouTube Channel](https://www.youtube.com/c/VelociraptorDFIR)\n* [Community auf Discord](https://www.velociraptor.app/community/)","src/content/knowledgebase/velociraptor.md","05636b9b97e61d17",{"html":243,"metadata":244},"\u003Cblockquote>\n\u003Cp>\u003Cstrong>⚠️ Hinweis\u003C/strong>: Dies ist ein vorläufiger, KI-generierter Knowledgebase-Eintrag. Wir freuen uns über Verbesserungen und Ergänzungen durch die Community!\u003C/p>\n\u003C/blockquote>\n\u003Ch1 id=\"übersicht\">Übersicht\u003C/h1>\n\u003Cp>Velociraptor ist ein Open-Source-Tool zur Endpoint-Forensik mit Fokus auf Skalierbarkeit, Präzision und Geschwindigkeit. Es ermöglicht die zielgerichtete Erfassung und Analyse digitaler Artefakte über eine eigene Query Language – VQL (Velociraptor Query Language). Die Architektur erlaubt remote Zugriff auf tausende Endpoints gleichzeitig, ohne dass vollständige Disk-Images erforderlich sind.\u003C/p>\n\u003Ch2 id=\"hauptmerkmale\">Hauptmerkmale\u003C/h2>\n\u003Cul>\n\u003Cli>🌐 Web-basierte Benutzeroberfläche\u003C/li>\n\u003Cli>💡 VQL – mächtige, SQL-ähnliche Abfragesprache\u003C/li>\n\u003Cli>🚀 Hochskalierbare Hunt-Funktionalität\u003C/li>\n\u003Cli>🔍 Artefaktbasierte Sammlung (ohne Full-Image)\u003C/li>\n\u003Cli>🖥️ Plattformunterstützung für Windows, macOS, Linux\u003C/li>\n\u003Cli>📦 Apache 2.0 Lizenz – Open Source\u003C/li>\n\u003C/ul>\n\u003Cp>Weitere Infos: \u003Ca href=\"https://www.velociraptor.app/\">velociraptor.app\u003C/a>\u003Cbr>\nProjektspiegel: \u003Ca href=\"https://raptor.cc24.dev\">raptor.cc24.dev\u003C/a>\u003Cbr>\nStatus: \u003Cimg src=\"https://status.mikoshi.de/api/badge/33/status\" alt=\"Status\">\u003C/p>\n\u003Chr>\n\u003Ch2 id=\"installation\">Installation\u003C/h2>\n\u003Ch3 id=\"voraussetzungen\">Voraussetzungen\u003C/h3>\n\u003Cul>\n\u003Cli>Python ≥ 3.9\u003C/li>\n\u003Cli>Adminrechte auf dem System\u003C/li>\n\u003Cli>Firewall-Freigaben für Webport (Standard: 8000)\u003C/li>\n\u003C/ul>\n\u003Ch3 id=\"installation-unter-linuxmacos\">Installation unter Linux/macOS\u003C/h3>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"bash\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">wget\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> https://github.com/Velocidex/velociraptor/releases/latest/download/velociraptor\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">chmod\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> +x\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> velociraptor\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">sudo\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> mv\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> velociraptor\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> /usr/local/bin/\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Ch3 id=\"installation-unter-windows\">Installation unter Windows\u003C/h3>\n\u003Col>\n\u003Cli>\n\u003Cp>Download der \u003Ccode>.exe\u003C/code> von der \u003Ca href=\"https://github.com/Velocidex/velociraptor/releases\">Release-Seite\u003C/a>\u003C/p>\n\u003C/li>\n\u003Cli>\n\u003Cp>Ausführung in PowerShell mit Adminrechten:\u003C/p>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"powershell\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#E1E4E8\">.\\\u003C/span>\u003Cspan style=\"color:#79B8FF\">velociraptor.exe\u003C/span>\u003Cspan style=\"color:#E1E4E8\"> config generate \u003C/span>\u003Cspan style=\"color:#F97583\">>\u003C/span>\u003Cspan style=\"color:#E1E4E8\"> server.config.yaml\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003C/li>\n\u003C/ol>\n\u003Chr>\n\u003Ch2 id=\"konfiguration\">Konfiguration\u003C/h2>\n\u003Ch3 id=\"server-setup\">Server Setup\u003C/h3>\n\u003Col>\n\u003Cli>\n\u003Cp>Generiere die Konfigurationsdatei:\u003C/p>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"bash\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">velociraptor\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> config\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> generate\u003C/span>\u003Cspan style=\"color:#F97583\"> >\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> server.config.yaml\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003C/li>\n\u003Cli>\n\u003Cp>Starte den Server:\u003C/p>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"bash\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">velociraptor\u003C/span>\u003Cspan style=\"color:#79B8FF\"> --config\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> server.config.yaml\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> frontend\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003C/li>\n\u003Cli>\n\u003Cp>Zugriff über Browser via \u003Ccode>https://<hostname>:8000\u003C/code>\u003C/p>\n\u003C/li>\n\u003C/ol>\n\u003Ch3 id=\"client-deployment\">Client Deployment\u003C/h3>\n\u003Cul>\n\u003Cli>MSI/EXE für Windows, oder \u003Ccode>deb/rpm\u003C/code> für Linux\u003C/li>\n\u003Cli>Unterstützt automatische Registrierung am Server\u003C/li>\n\u003Cli>Deployment über GPO, Puppet, Ansible etc. möglich\u003C/li>\n\u003C/ul>\n\u003Chr>\n\u003Ch2 id=\"verwendungsbeispiele\">Verwendungsbeispiele\u003C/h2>\n\u003Ch3 id=\"1-live-memory-artefakte-sammeln\">1. Live-Memory-Artefakte sammeln\u003C/h3>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"plaintext\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan>SELECT * FROM Artifact.MemoryInfo()\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Ch3 id=\"2-hunt-starten-auf-verdächtige-prozesse\">2. Hunt starten auf verdächtige Prozesse\u003C/h3>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"plaintext\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan>SELECT * FROM pslist()\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan>WHERE Name =~ \"mimikatz|cobaltstrike\"\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Ch3 id=\"3-dateiinhalt-extrahieren\">3. Dateiinhalt extrahieren\u003C/h3>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"plaintext\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan>SELECT * FROM glob(globs=\"C:\\\\Users\\\\*\\\\AppData\\\\*.dat\")\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Chr>\n\u003Ch2 id=\"best-practices\">Best Practices\u003C/h2>\n\u003Cul>\n\u003Cli>Erstelle eigene Artefakte für unternehmensspezifische Bedrohungsmodelle\u003C/li>\n\u003Cli>Verwende “Notebook”-Funktion für strukturierte Analysen\u003C/li>\n\u003Cli>Nutze “Labels”, um Endpoints zu organisieren (z. B. \u003Ccode>location:Berlin\u003C/code>)\u003C/li>\n\u003Cli>Kombiniere Velociraptor mit SIEM/EDR-Systemen über REST API\u003C/li>\n\u003C/ul>\n\u003Chr>\n\u003Ch2 id=\"troubleshooting\">Troubleshooting\u003C/h2>\n\u003Ch3 id=\"problem-keine-verbindung-vom-client-zum-server\">Problem: Keine Verbindung vom Client zum Server\u003C/h3>\n\u003Cp>\u003Cstrong>Lösung:\u003C/strong>\u003C/p>\n\u003Cul>\n\u003Cli>Ports freigegeben? (Default: 8000/tcp)\u003C/li>\n\u003Cli>TLS-Zertifikate korrekt generiert?\u003C/li>\n\u003Cli>\u003Ccode>server.config.yaml\u003C/code> auf korrekte \u003Ccode>public_ip\u003C/code> prüfen\u003C/li>\n\u003C/ul>\n\u003Ch3 id=\"problem-hunt-hängt-in-warteschleife\">Problem: Hunt hängt in Warteschleife\u003C/h3>\n\u003Cp>\u003Cstrong>Lösung:\u003C/strong>\u003C/p>\n\u003Cul>\n\u003Cli>Genügend Worker-Prozesse aktiv?\u003C/li>\n\u003Cli>Endpoint online?\u003C/li>\n\u003Cli>\u003Ccode>log_level\u003C/code> auf \u003Ccode>debug\u003C/code> setzen und Log analysieren\u003C/li>\n\u003C/ul>\n\u003Chr>\n\u003Ch2 id=\"weiterführende-themen\">Weiterführende Themen\u003C/h2>\n\u003Cul>\n\u003Cli>Eigene Artefakte schreiben mit VQL\u003C/li>\n\u003Cli>Integration mit ELK Stack\u003C/li>\n\u003Cli>Automatisiertes Incident Response Playbook\u003C/li>\n\u003Cli>Velociraptor als IR-as-a-Service einsetzen\u003C/li>\n\u003C/ul>\n\u003Chr>\n\u003Cp>🧠 \u003Cstrong>Tipp:\u003C/strong> Die Lernkurve bei VQL ist steil – aber mit hohem ROI. Testumgebung aufsetzen und mit Community-Artefakten starten.\u003C/p>\n\u003Cp>📚 Weitere Ressourcen:\u003C/p>\n\u003Cul>\n\u003Cli>\u003Ca href=\"https://docs.velociraptor.app/\">Offizielle Doku\u003C/a>\u003C/li>\n\u003Cli>\u003Ca href=\"https://www.youtube.com/c/VelociraptorDFIR\">YouTube Channel\u003C/a>\u003C/li>\n\u003Cli>\u003Ca href=\"https://www.velociraptor.app/community/\">Community auf Discord\u003C/a>\u003C/li>\n\u003C/ul>",{"headings":245,"localImagePaths":284,"remoteImagePaths":285,"frontmatter":225,"imagePaths":286},[246,247,250,251,252,255,258,259,262,265,266,269,272,275,276,277,280,283],{"depth":43,"slug":44,"text":45},{"depth":47,"slug":248,"text":249},"hauptmerkmale","Hauptmerkmale",{"depth":47,"slug":48,"text":49},{"depth":51,"slug":52,"text":53},{"depth":51,"slug":253,"text":254},"installation-unter-linuxmacos","Installation unter Linux/macOS",{"depth":51,"slug":256,"text":257},"installation-unter-windows","Installation unter Windows",{"depth":47,"slug":58,"text":59},{"depth":51,"slug":260,"text":261},"server-setup","Server Setup",{"depth":51,"slug":263,"text":264},"client-deployment","Client Deployment",{"depth":47,"slug":70,"text":71},{"depth":51,"slug":267,"text":268},"1-live-memory-artefakte-sammeln","1. Live-Memory-Artefakte sammeln",{"depth":51,"slug":270,"text":271},"2-hunt-starten-auf-verdächtige-prozesse","2. Hunt starten auf verdächtige Prozesse",{"depth":51,"slug":273,"text":274},"3-dateiinhalt-extrahieren","3. Dateiinhalt extrahieren",{"depth":47,"slug":79,"text":80},{"depth":47,"slug":82,"text":83},{"depth":51,"slug":278,"text":279},"problem-keine-verbindung-vom-client-zum-server","Problem: Keine Verbindung vom Client zum Server",{"depth":51,"slug":281,"text":282},"problem-hunt-hängt-in-warteschleife","Problem: Hunt hängt in Warteschleife",{"depth":47,"slug":94,"text":95},[],[],[],"velociraptor.md","android-logical-imaging",{"id":288,"data":290,"body":303,"filePath":304,"digest":305,"rendered":306,"legacyId":534},{"title":291,"tool_name":292,"description":293,"last_updated":294,"author":295,"difficulty":230,"categories":296,"tags":298,"sections":302,"review_status":34},"Extraktion logischer Dateisysteme alter Android-Smartphones - eine KI-Recherche","Android Logical Imaging","Wie man alte Android-Handys aufbekommen könnte - eine Recherche von Claude",["Date","2025-07-21T00:00:00.000Z"],"Claude 4 Sonnet (Research)",[297],"data-collection",[299,300,301],"imaging","filesystem","hardware-interface",{"overview":32,"installation":32,"configuration":32,"usage_examples":32,"best_practices":32,"troubleshooting":32,"advanced_topics":32},"# Übersicht\n\nOpen-Source Android Forensik bietet robuste Alternativen zu kommerziellen Lösungen wie Cellebrite UFED und Magnet AXIOM. Besonders für ältere Android-Geräte (5+ Jahre) existieren bewährte Methoden zur Datenextraktion und -analyse.\n\n## Kernkomponenten des Open-Source Forensik-Stacks\n\n**Autopsy Digital Forensics Platform** bildet das Fundament mit GUI-basierter Analyse und integrierten Android-Parsing-Fähigkeiten. Die Plattform unterstützt **ALEAPP (Android Logs Events And Protobuf Parser)**, das über 100 Artefakt-Kategorien aus Android-Extraktionen parst.\n\n**Mobile Verification Toolkit (MVT)** von Amnesty International bietet spezialisierte Command-Line-Tools für Android-Analyse mit Fokus auf Kompromittierungserkennung.\n\n**SIFT Workstation** stellt eine komplette Ubuntu-basierte forensische Umgebung mit 125+ vorinstallierten Tools bereit.\n\n## Erfolgsraten nach Gerätealter\n\n- **Pre-2017 Geräte**: 85-98% logische Extraktion, 30-70% physische Extraktion\n- **2017-2019 Geräte**: 80-95% logische Extraktion, 15-35% physische Extraktion  \n- **2020+ Geräte**: 70-85% logische Extraktion, 5-15% physische Extraktion\n\n# Installation\n\n## SIFT Workstation Setup\n\n### Systemanforderungen\n- Quad-Core CPU 2.5GHz+\n- 16GB+ RAM\n- 500GB+ SSD Speicher\n- USB 3.0+ Anschlüsse\n\n### Installation\n1. Download von [SANS SIFT Workstation](https://www.sans.org/tools/sift-workstation/)\n2. VMware/VirtualBox Import der OVA-Datei\n3. VM-Konfiguration: 8GB+ RAM, 4+ CPU-Kerne\n\n```bash\n# Update nach Installation\nsudo apt update && sudo apt upgrade -y\nsudo sift update\n```\n\n## Autopsy Installation\n\n### Windows Installation\n1. Download von [autopsy.com](https://www.autopsy.com/)\n2. Java 8+ Installation erforderlich\n3. Installation mit Administratorrechten\n\n### Linux Installation\n```bash\n# Ubuntu/Debian\nsudo apt install autopsy sleuthkit\n# Oder manueller Download und Installation\nwget https://github.com/sleuthkit/autopsy/releases/latest\n```\n\n## Essential Tools Installation\n\n### Android Debug Bridge (ADB)\n```bash\n# Ubuntu/Debian\nsudo apt install android-tools-adb android-tools-fastboot\n\n# Windows - Download Android Platform Tools\n# https://developer.android.com/studio/releases/platform-tools\n```\n\n### ALEAPP Installation\n```bash\ngit clone https://github.com/abrignoni/ALEAPP.git\ncd ALEAPP\npip3 install -r requirements.txt\n```\n\n### Mobile Verification Toolkit (MVT)\n```bash\npip3 install mvt\n# Oder via GitHub für neueste Version\ngit clone https://github.com/mvt-project/mvt.git\ncd mvt && pip3 install .\n```\n\n### Andriller Installation\n```bash\ngit clone https://github.com/den4uk/andriller.git\ncd andriller\npip3 install -r requirements.txt\n```\n\n# Konfiguration\n\n## ADB Setup und Gerätevorbereitung\n\n### USB-Debugging aktivieren\n1. Entwickleroptionen freischalten (7x Build-Nummer antippen)\n2. USB-Debugging aktivieren\n3. Gerät via USB verbinden\n4. RSA-Fingerprint akzeptieren\n\n### ADB Verbindung testen\n```bash\nadb devices\n# Sollte Gerät mit \"device\" Status zeigen\nadb shell getprop ro.build.version.release  # Android Version\nadb shell getprop ro.product.model         # Gerätemodell\n```\n\n## Autopsy Projektkonfiguration\n\n### Case-Setup\n1. Neuen Fall erstellen\n2. Ermittler-Informationen eingeben\n3. Case-Verzeichnis festlegen (ausreichend Speicherplatz)\n\n### Android Analyzer Module aktivieren\n- Tools → Options → Modules\n- Android Analyzer aktivieren\n- ALEAPP Integration konfigurieren\n\n### Hash-Algorithmen konfigurieren\n- MD5, SHA-1, SHA-256 für Integritätsprüfung\n- Automatische Hash-Berechnung bei Import aktivieren\n\n## MVT Konfiguration\n\n### Konfigurationsdatei erstellen\n```yaml\n# ~/.mvt/config.yaml\nadb_path: \"/usr/bin/adb\"\noutput_folder: \"/home/user/mvt_output\"\n```\n\n# Verwendungsbeispiele\n\n## Fall 1: Logische Datenextraktion mit ADB\n\n### Geräteinformationen sammeln\n```bash\n# Systeminfo\nadb shell getprop > device_properties.txt\nadb shell cat /proc/version > kernel_info.txt\nadb shell mount > mount_info.txt\n\n# Installierte Apps\nadb shell pm list packages -f > installed_packages.txt\n```\n\n### Datenbank-Extraktion\n```bash\n# SMS/MMS Datenbank\nadb pull /data/data/com.android.providers.telephony/databases/mmssms.db\n\n# Kontakte\nadb pull /data/data/com.android.providers.contacts/databases/contacts2.db\n\n# Anrufliste  \nadb pull /data/data/com.android.providers.contacts/databases/calllog.db\n```\n\n### WhatsApp Datenextraktion\n```bash\n# WhatsApp Datenbanken (Root erforderlich)\nadb shell su -c \"cp -r /data/data/com.whatsapp/ /sdcard/whatsapp_backup/\"\nadb pull /sdcard/whatsapp_backup/\n```\n\n## Fall 2: Android Backup-Analyse\n\n### Vollständiges Backup erstellen\n```bash\n# Umfassendes Backup (ohne Root)\nadb backup -all -system -apk -shared -f backup.ab\n\n# Backup entschlüsseln (falls verschlüsselt)\njava -jar abe.jar unpack backup.ab backup.tar\ntar -xf backup.tar\n```\n\n### Backup mit ALEAPP analysieren\n```bash\npython3 aleappGUI.py\n# Oder Command-Line\npython3 aleapp.py -t tar -i backup.tar -o output_folder\n```\n\n## Fall 3: MVT Kompromittierungsanalyse\n\n### Live-Geräteanalyse\n```bash\n# ADB-basierte Analyse\nmvt-android check-adb --output /path/to/output/\n\n# Backup-Analyse\nmvt-android check-backup --output /path/to/output/ backup.ab\n```\n\n### IOC-Suche mit Pegasus-Indikatoren\n```bash\n# Mit vorgefertigten IOCs\nmvt-android check-adb --iocs /path/to/pegasus.stix2 --output results/\n```\n\n## Fall 4: Physische Extraktion (Root erforderlich)\n\n### Device Rooting - MediaTek Geräte\n```bash\n# MTKClient für MediaTek-Chipsets\ngit clone https://github.com/bkerler/mtkclient.git\ncd mtkclient\npython3 mtk payload\n\n# Nach erfolgreichem Root\nadb shell su\n```\n\n### Vollständiges Memory Dump\n```bash\n# Partitionslayout ermitteln\nadb shell su -c \"cat /proc/partitions\"\nadb shell su -c \"ls -la /dev/block/\"\n\n# Vollständiges Device Image (Root erforderlich)\nadb shell su -c \"dd if=/dev/block/mmcblk0 of=/sdcard/full_device.img bs=4096\"\nadb pull /sdcard/full_device.img\n```\n\n# Best Practices\n\n## Rechtliche Compliance\n\n### Dokumentation und Chain of Custody\n- **Vollständige Dokumentation**: Wer, Was, Wann, Wo, Warum\n- **Hash-Verifikation**: MD5/SHA-256 für alle extrahierten Daten\n- **Nur forensische Kopien analysieren**, niemals Originaldaten\n- **Schriftliche Genehmigung** für Geräteanalyse einholen\n\n### Familiengeräte und Nachlässe\n- Genehmigung durch Nachlassverwalter erforderlich\n- Gerichtsbeschlüsse für Cloud-Zugang eventuell nötig\n- Drittpartei-Kommunikation kann weiterhin geschützt sein\n\n## Technische Best Practices\n\n### Hash-Integrität sicherstellen\n```bash\n# Hash vor und nach Transfer prüfen\nmd5sum original_file.db\nsha256sum original_file.db\n\n# Hash-Verifikation dokumentieren\necho \"$(date): MD5: $(md5sum file.db)\" >> chain_of_custody.log\n```\n\n### Sichere Arbeitsumgebung\n- Isolierte VM für Forensik-Arbeit\n- Netzwerk-Isolation während Analyse\n- Verschlüsselte Speicherung aller Evidenz\n- Regelmäßige Backups der Case-Datenbanken\n\n### Qualitätssicherung\n- Peer-Review kritischer Analysen\n- Standardisierte Arbeitsabläufe (SOPs)\n- Regelmäßige Tool-Validierung\n- Kontinuierliche Weiterbildung\n\n## Erfolgsmaximierung nach Gerätehersteller\n\n### MediaTek-Geräte (Höchste Erfolgsrate)\n- BootROM-Exploits für MT6735, MT6737, MT6750, MT6753, MT6797\n- MTKClient für Hardware-Level-Zugang\n- Erfolgsrate: 80%+ für Geräte 2015-2019\n\n### Samsung-Geräte\n- Ältere Knox-Implementierungen umgehbar\n- Emergency Dialer Exploits für Android 4.x\n- Erfolgsrate: 40-70% je nach Knox-Version\n\n### Pixel/Nexus-Geräte\n- Bootloader-Unlocking oft möglich\n- Fastboot-basierte Recovery-Installation\n- Erfolgsrate: 60-80% bei freigeschaltetem Bootloader\n\n# Troubleshooting\n\n## Problem: ADB erkennt Gerät nicht\n\n### Lösung: USB-Treiber und Berechtigungen\n```bash\n# Linux: USB-Berechtigungen prüfen\nlsusb | grep -i android\nsudo chmod 666 /dev/bus/usb/XXX/XXX\n\n# udev-Regeln erstellen\necho 'SUBSYSTEM==\"usb\", ATTR{idVendor}==\"18d1\", MODE=\"0666\", GROUP=\"plugdev\"' | sudo tee /etc/udev/rules.d/51-android.rules\nsudo udevadm control --reload-rules\n```\n\n### Windows: Treiber-Installation\n1. Geräte-Manager öffnen\n2. Android-Gerät mit Warnsymbol finden\n3. Treiber manuell installieren (Android USB Driver)\n\n## Problem: Verschlüsselte Android Backups\n\n### Lösung: Android Backup Extractor\n```bash\n# ADB Backup Extractor installieren\ngit clone https://github.com/nelenkov/android-backup-extractor.git\ncd android-backup-extractor\ngradle build\n\n# Backup entschlüsseln\njava -jar abe.jar unpack backup.ab backup.tar [password]\n```\n\n## Problem: Unzureichende Berechtigungen für Datenextraktion\n\n### Lösung: Alternative Extraktionsmethoden\n```bash\n# AFLogical OSE für begrenzte Extraktion ohne Root\n# WhatsApp Key/DB Extractor für spezifische Apps\n# Backup-basierte Extraktion als Fallback\n\n# Custom Recovery für erweiterten Zugang\nfastboot flash recovery twrp-device.img\n```\n\n## Problem: ALEAPP Parsing-Fehler\n\n### Lösung: Datenformat-Probleme beheben\n```bash\n# Log-Dateien prüfen\npython3 aleapp.py -t dir -i /path/to/data -o output --debug\n\n# Spezifische Parser deaktivieren\n# Manuelle SQLite-Analyse bei Parser-Fehlern\nsqlite3 database.db \".tables\"\nsqlite3 database.db \".schema table_name\"\n```\n\n# Erweiterte Techniken\n\n## Memory Forensics mit LiME\n\n### LiME für ARM-Devices kompilieren\n```bash\n# Cross-Compilation Setup\nexport ARCH=arm\nexport CROSS_COMPILE=arm-linux-gnueabi-\nexport KERNEL_DIR=/path/to/kernel/source\n\n# LiME Module kompilieren\ngit clone https://github.com/504ensicsLabs/LiME.git\ncd LiME/src\nmake\n\n# Memory Dump erstellen (Root erforderlich)\nadb push lime.ko /data/local/tmp/\nadb shell su -c \"insmod /data/local/tmp/lime.ko 'path=/sdcard/memory.lime format=lime'\"\n```\n\n### Volatility-Analyse von Android Memory\n```bash\n# Memory Dump analysieren\npython vol.py -f memory.lime --profile=Linux \u003Cprofile> linux.pslist\npython vol.py -f memory.lime --profile=Linux \u003Cprofile> linux.bash\npython vol.py -f memory.lime --profile=Linux \u003Cprofile> linux.netstat\n```\n\n## FRIDA-basierte Runtime-Analyse\n\n### FRIDA für Kryptographie-Hooks\n```javascript\n// crypto_hooks.js - SSL/TLS Traffic abfangen\nJava.perform(function() {\n    var SSLContext = Java.use(\"javax.net.ssl.SSLContext\");\n    SSLContext.init.overload('[Ljavax.net.ssl.KeyManager;', '[Ljavax.net.ssl.TrustManager;', 'java.security.SecureRandom').implementation = function(keyManagers, trustManagers, secureRandom) {\n        console.log(\"[+] SSLContext.init() called\");\n        this.init(keyManagers, trustManagers, secureRandom);\n    };\n});\n```\n\n### FRIDA Installation und Verwendung\n```bash\n# FRIDA Server auf Android-Gerät installieren\nadb push frida-server /data/local/tmp/\nadb shell su -c \"chmod 755 /data/local/tmp/frida-server\"\nadb shell su -c \"/data/local/tmp/frida-server &\"\n\n# Script ausführen\nfrida -U -l crypto_hooks.js com.target.package\n```\n\n## Custom Recovery und Fastboot-Exploits\n\n### TWRP Installation für forensischen Zugang\n```bash\n# Bootloader entsperren (Herstellerabhängig)\nfastboot oem unlock\n# Oder\nfastboot flashing unlock\n\n# TWRP flashen\nfastboot flash recovery twrp-device.img\nfastboot boot twrp-device.img  # Temporäre Installation\n\n# In TWRP: ADB-Zugang mit Root-Berechtigungen\nadb shell mount /system\nadb shell mount /data\n```\n\n### Partitions-Imaging mit dd\n```bash\n# Vollständige Partition-Liste\nadb shell cat /proc/partitions\n\n# Kritische Partitionen extrahieren\nadb shell dd if=/dev/block/bootdevice/by-name/system of=/external_sd/system.img\nadb shell dd if=/dev/block/bootdevice/by-name/userdata of=/external_sd/userdata.img\nadb shell dd if=/dev/block/bootdevice/by-name/boot of=/external_sd/boot.img\n```\n\n## SQLite Forensics und gelöschte Daten\n\n### Erweiterte SQLite-Analyse\n```bash\n# Freelist-Analyse für gelöschte Einträge\nsqlite3 database.db \"PRAGMA freelist_count;\"\nsqlite3 database.db \"PRAGMA page_size;\"\n\n# WAL-Datei Analyse\nsqlite3 database.db \"PRAGMA wal_checkpoint;\"\nstrings database.db-wal | grep -i \"search_term\"\n\n# Undark für Deleted Record Recovery\nundark database.db --freelist --export-csv\n```\n\n### Timeline-Rekonstruktion\n```bash\n# Autopsy Timeline-Generierung\n# Tools → Generate Timeline\n# Analyse von MAC-Times (Modified, Accessed, Created)\n\n# Plaso Timeline-Tools\nlog2timeline.py timeline.plaso /path/to/android/data/\npsort.py -o dynamic timeline.plaso\n```\n\n## Weiterführende Ressourcen\n\n### Dokumentation und Standards\n- [NIST SP 800-101 Rev. 1 - Mobile Device Forensics Guidelines](https://csrc.nist.gov/pubs/sp/800/101/r1/final)\n- [SANS FOR585 - Smartphone Forensics](https://www.sans.org/cyber-security-courses/advanced-smartphone-mobile-device-forensics/)\n- [ALEAPP GitHub Repository](https://github.com/abrignoni/ALEAPP)\n- [MVT Documentation](https://docs.mvt.re/en/latest/)\n\n### Community und Weiterbildung\n- [Autopsy User Documentation](https://sleuthkit.org/autopsy/docs/)\n- [Android Forensics References](https://github.com/impillar/AndroidReferences/blob/master/AndroidTools.md)\n- [Digital Forensics Framework Collection](https://github.com/mesquidar/ForensicsTools)\n\n### Spezialisierte Tools\n- [MTKClient für MediaTek Exploits](https://github.com/bkerler/mtkclient)\n- [Android Forensics Framework](https://github.com/nowsecure/android-forensics)\n- [Santoku Linux Mobile Forensics Distribution](https://santoku-linux.com/)\n\n---\n\n**Wichtiger Hinweis**: Diese Anleitung dient ausschließlich für autorisierte forensische Untersuchungen. Stellen Sie sicher, dass Sie über entsprechende rechtliche Befugnisse verfügen, bevor Sie diese Techniken anwenden. Bei Zweifeln konsultieren Sie Rechtsberatung.","src/content/knowledgebase/android-logical-imaging.md","0bb3f1d2c872d2bf",{"html":307,"metadata":308},"\u003Ch1 id=\"übersicht\">Übersicht\u003C/h1>\n\u003Cp>Open-Source Android Forensik bietet robuste Alternativen zu kommerziellen Lösungen wie Cellebrite UFED und Magnet AXIOM. Besonders für ältere Android-Geräte (5+ Jahre) existieren bewährte Methoden zur Datenextraktion und -analyse.\u003C/p>\n\u003Ch2 id=\"kernkomponenten-des-open-source-forensik-stacks\">Kernkomponenten des Open-Source Forensik-Stacks\u003C/h2>\n\u003Cp>\u003Cstrong>Autopsy Digital Forensics Platform\u003C/strong> bildet das Fundament mit GUI-basierter Analyse und integrierten Android-Parsing-Fähigkeiten. Die Plattform unterstützt \u003Cstrong>ALEAPP (Android Logs Events And Protobuf Parser)\u003C/strong>, das über 100 Artefakt-Kategorien aus Android-Extraktionen parst.\u003C/p>\n\u003Cp>\u003Cstrong>Mobile Verification Toolkit (MVT)\u003C/strong> von Amnesty International bietet spezialisierte Command-Line-Tools für Android-Analyse mit Fokus auf Kompromittierungserkennung.\u003C/p>\n\u003Cp>\u003Cstrong>SIFT Workstation\u003C/strong> stellt eine komplette Ubuntu-basierte forensische Umgebung mit 125+ vorinstallierten Tools bereit.\u003C/p>\n\u003Ch2 id=\"erfolgsraten-nach-gerätealter\">Erfolgsraten nach Gerätealter\u003C/h2>\n\u003Cul>\n\u003Cli>\u003Cstrong>Pre-2017 Geräte\u003C/strong>: 85-98% logische Extraktion, 30-70% physische Extraktion\u003C/li>\n\u003Cli>\u003Cstrong>2017-2019 Geräte\u003C/strong>: 80-95% logische Extraktion, 15-35% physische Extraktion\u003C/li>\n\u003Cli>\u003Cstrong>2020+ Geräte\u003C/strong>: 70-85% logische Extraktion, 5-15% physische Extraktion\u003C/li>\n\u003C/ul>\n\u003Ch1 id=\"installation\">Installation\u003C/h1>\n\u003Ch2 id=\"sift-workstation-setup\">SIFT Workstation Setup\u003C/h2>\n\u003Ch3 id=\"systemanforderungen\">Systemanforderungen\u003C/h3>\n\u003Cul>\n\u003Cli>Quad-Core CPU 2.5GHz+\u003C/li>\n\u003Cli>16GB+ RAM\u003C/li>\n\u003Cli>500GB+ SSD Speicher\u003C/li>\n\u003Cli>USB 3.0+ Anschlüsse\u003C/li>\n\u003C/ul>\n\u003Ch3 id=\"installation-1\">Installation\u003C/h3>\n\u003Col>\n\u003Cli>Download von \u003Ca href=\"https://www.sans.org/tools/sift-workstation/\">SANS SIFT Workstation\u003C/a>\u003C/li>\n\u003Cli>VMware/VirtualBox Import der OVA-Datei\u003C/li>\n\u003Cli>VM-Konfiguration: 8GB+ RAM, 4+ CPU-Kerne\u003C/li>\n\u003C/ol>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"bash\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># Update nach Installation\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">sudo\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> apt\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> update\u003C/span>\u003Cspan style=\"color:#E1E4E8\"> && \u003C/span>\u003Cspan style=\"color:#B392F0\">sudo\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> apt\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> upgrade\u003C/span>\u003Cspan style=\"color:#79B8FF\"> -y\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">sudo\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> sift\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> update\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Ch2 id=\"autopsy-installation\">Autopsy Installation\u003C/h2>\n\u003Ch3 id=\"windows-installation\">Windows Installation\u003C/h3>\n\u003Col>\n\u003Cli>Download von \u003Ca href=\"https://www.autopsy.com/\">autopsy.com\u003C/a>\u003C/li>\n\u003Cli>Java 8+ Installation erforderlich\u003C/li>\n\u003Cli>Installation mit Administratorrechten\u003C/li>\n\u003C/ol>\n\u003Ch3 id=\"linux-installation\">Linux Installation\u003C/h3>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"bash\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># Ubuntu/Debian\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">sudo\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> apt\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> install\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> autopsy\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> sleuthkit\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># Oder manueller Download und Installation\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">wget\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> https://github.com/sleuthkit/autopsy/releases/latest\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Ch2 id=\"essential-tools-installation\">Essential Tools Installation\u003C/h2>\n\u003Ch3 id=\"android-debug-bridge-adb\">Android Debug Bridge (ADB)\u003C/h3>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"bash\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># Ubuntu/Debian\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">sudo\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> apt\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> install\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> android-tools-adb\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> android-tools-fastboot\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># Windows - Download Android Platform Tools\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># https://developer.android.com/studio/releases/platform-tools\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Ch3 id=\"aleapp-installation\">ALEAPP Installation\u003C/h3>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"bash\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">git\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> clone\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> https://github.com/abrignoni/ALEAPP.git\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#79B8FF\">cd\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> ALEAPP\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">pip3\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> install\u003C/span>\u003Cspan style=\"color:#79B8FF\"> -r\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> requirements.txt\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Ch3 id=\"mobile-verification-toolkit-mvt\">Mobile Verification Toolkit (MVT)\u003C/h3>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"bash\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">pip3\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> install\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> mvt\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># Oder via GitHub für neueste Version\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">git\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> clone\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> https://github.com/mvt-project/mvt.git\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#79B8FF\">cd\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> mvt\u003C/span>\u003Cspan style=\"color:#E1E4E8\"> && \u003C/span>\u003Cspan style=\"color:#B392F0\">pip3\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> install\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> .\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Ch3 id=\"andriller-installation\">Andriller Installation\u003C/h3>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"bash\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">git\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> clone\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> https://github.com/den4uk/andriller.git\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#79B8FF\">cd\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> andriller\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">pip3\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> install\u003C/span>\u003Cspan style=\"color:#79B8FF\"> -r\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> requirements.txt\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Ch1 id=\"konfiguration\">Konfiguration\u003C/h1>\n\u003Ch2 id=\"adb-setup-und-gerätevorbereitung\">ADB Setup und Gerätevorbereitung\u003C/h2>\n\u003Ch3 id=\"usb-debugging-aktivieren\">USB-Debugging aktivieren\u003C/h3>\n\u003Col>\n\u003Cli>Entwickleroptionen freischalten (7x Build-Nummer antippen)\u003C/li>\n\u003Cli>USB-Debugging aktivieren\u003C/li>\n\u003Cli>Gerät via USB verbinden\u003C/li>\n\u003Cli>RSA-Fingerprint akzeptieren\u003C/li>\n\u003C/ol>\n\u003Ch3 id=\"adb-verbindung-testen\">ADB Verbindung testen\u003C/h3>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"bash\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">adb\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> devices\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># Sollte Gerät mit \"device\" Status zeigen\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">adb\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> shell\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> getprop\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> ro.build.version.release\u003C/span>\u003Cspan style=\"color:#6A737D\">  # Android Version\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">adb\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> shell\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> getprop\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> ro.product.model\u003C/span>\u003Cspan style=\"color:#6A737D\">         # Gerätemodell\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Ch2 id=\"autopsy-projektkonfiguration\">Autopsy Projektkonfiguration\u003C/h2>\n\u003Ch3 id=\"case-setup\">Case-Setup\u003C/h3>\n\u003Col>\n\u003Cli>Neuen Fall erstellen\u003C/li>\n\u003Cli>Ermittler-Informationen eingeben\u003C/li>\n\u003Cli>Case-Verzeichnis festlegen (ausreichend Speicherplatz)\u003C/li>\n\u003C/ol>\n\u003Ch3 id=\"android-analyzer-module-aktivieren\">Android Analyzer Module aktivieren\u003C/h3>\n\u003Cul>\n\u003Cli>Tools → Options → Modules\u003C/li>\n\u003Cli>Android Analyzer aktivieren\u003C/li>\n\u003Cli>ALEAPP Integration konfigurieren\u003C/li>\n\u003C/ul>\n\u003Ch3 id=\"hash-algorithmen-konfigurieren\">Hash-Algorithmen konfigurieren\u003C/h3>\n\u003Cul>\n\u003Cli>MD5, SHA-1, SHA-256 für Integritätsprüfung\u003C/li>\n\u003Cli>Automatische Hash-Berechnung bei Import aktivieren\u003C/li>\n\u003C/ul>\n\u003Ch2 id=\"mvt-konfiguration\">MVT Konfiguration\u003C/h2>\n\u003Ch3 id=\"konfigurationsdatei-erstellen\">Konfigurationsdatei erstellen\u003C/h3>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"yaml\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># ~/.mvt/config.yaml\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#85E89D\">adb_path\u003C/span>\u003Cspan style=\"color:#E1E4E8\">: \u003C/span>\u003Cspan style=\"color:#9ECBFF\">\"/usr/bin/adb\"\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#85E89D\">output_folder\u003C/span>\u003Cspan style=\"color:#E1E4E8\">: \u003C/span>\u003Cspan style=\"color:#9ECBFF\">\"/home/user/mvt_output\"\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Ch1 id=\"verwendungsbeispiele\">Verwendungsbeispiele\u003C/h1>\n\u003Ch2 id=\"fall-1-logische-datenextraktion-mit-adb\">Fall 1: Logische Datenextraktion mit ADB\u003C/h2>\n\u003Ch3 id=\"geräteinformationen-sammeln\">Geräteinformationen sammeln\u003C/h3>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"bash\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># Systeminfo\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">adb\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> shell\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> getprop\u003C/span>\u003Cspan style=\"color:#F97583\"> >\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> device_properties.txt\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">adb\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> shell\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> cat\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> /proc/version\u003C/span>\u003Cspan style=\"color:#F97583\"> >\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> kernel_info.txt\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">adb\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> shell\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> mount\u003C/span>\u003Cspan style=\"color:#F97583\"> >\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> mount_info.txt\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># Installierte Apps\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">adb\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> shell\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> pm\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> list\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> packages\u003C/span>\u003Cspan style=\"color:#79B8FF\"> -f\u003C/span>\u003Cspan style=\"color:#F97583\"> >\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> installed_packages.txt\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Ch3 id=\"datenbank-extraktion\">Datenbank-Extraktion\u003C/h3>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"bash\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># SMS/MMS Datenbank\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">adb\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> pull\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> /data/data/com.android.providers.telephony/databases/mmssms.db\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># Kontakte\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">adb\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> pull\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> /data/data/com.android.providers.contacts/databases/contacts2.db\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># Anrufliste  \u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">adb\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> pull\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> /data/data/com.android.providers.contacts/databases/calllog.db\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Ch3 id=\"whatsapp-datenextraktion\">WhatsApp Datenextraktion\u003C/h3>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"bash\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># WhatsApp Datenbanken (Root erforderlich)\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">adb\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> shell\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> su\u003C/span>\u003Cspan style=\"color:#79B8FF\"> -c\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> \"cp -r /data/data/com.whatsapp/ /sdcard/whatsapp_backup/\"\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">adb\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> pull\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> /sdcard/whatsapp_backup/\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Ch2 id=\"fall-2-android-backup-analyse\">Fall 2: Android Backup-Analyse\u003C/h2>\n\u003Ch3 id=\"vollständiges-backup-erstellen\">Vollständiges Backup erstellen\u003C/h3>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"bash\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># Umfassendes Backup (ohne Root)\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">adb\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> backup\u003C/span>\u003Cspan style=\"color:#79B8FF\"> -all\u003C/span>\u003Cspan style=\"color:#79B8FF\"> -system\u003C/span>\u003Cspan style=\"color:#79B8FF\"> -apk\u003C/span>\u003Cspan style=\"color:#79B8FF\"> -shared\u003C/span>\u003Cspan style=\"color:#79B8FF\"> -f\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> backup.ab\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># Backup entschlüsseln (falls verschlüsselt)\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">java\u003C/span>\u003Cspan style=\"color:#79B8FF\"> -jar\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> abe.jar\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> unpack\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> backup.ab\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> backup.tar\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">tar\u003C/span>\u003Cspan style=\"color:#79B8FF\"> -xf\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> backup.tar\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Ch3 id=\"backup-mit-aleapp-analysieren\">Backup mit ALEAPP analysieren\u003C/h3>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"bash\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">python3\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> aleappGUI.py\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># Oder Command-Line\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">python3\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> aleapp.py\u003C/span>\u003Cspan style=\"color:#79B8FF\"> -t\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> tar\u003C/span>\u003Cspan style=\"color:#79B8FF\"> -i\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> backup.tar\u003C/span>\u003Cspan style=\"color:#79B8FF\"> -o\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> output_folder\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Ch2 id=\"fall-3-mvt-kompromittierungsanalyse\">Fall 3: MVT Kompromittierungsanalyse\u003C/h2>\n\u003Ch3 id=\"live-geräteanalyse\">Live-Geräteanalyse\u003C/h3>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"bash\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># ADB-basierte Analyse\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">mvt-android\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> check-adb\u003C/span>\u003Cspan style=\"color:#79B8FF\"> --output\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> /path/to/output/\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># Backup-Analyse\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">mvt-android\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> check-backup\u003C/span>\u003Cspan style=\"color:#79B8FF\"> --output\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> /path/to/output/\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> backup.ab\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Ch3 id=\"ioc-suche-mit-pegasus-indikatoren\">IOC-Suche mit Pegasus-Indikatoren\u003C/h3>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"bash\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># Mit vorgefertigten IOCs\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">mvt-android\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> check-adb\u003C/span>\u003Cspan style=\"color:#79B8FF\"> --iocs\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> /path/to/pegasus.stix2\u003C/span>\u003Cspan style=\"color:#79B8FF\"> --output\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> results/\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Ch2 id=\"fall-4-physische-extraktion-root-erforderlich\">Fall 4: Physische Extraktion (Root erforderlich)\u003C/h2>\n\u003Ch3 id=\"device-rooting---mediatek-geräte\">Device Rooting - MediaTek Geräte\u003C/h3>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"bash\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># MTKClient für MediaTek-Chipsets\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">git\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> clone\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> https://github.com/bkerler/mtkclient.git\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#79B8FF\">cd\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> mtkclient\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">python3\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> mtk\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> payload\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># Nach erfolgreichem Root\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">adb\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> shell\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> su\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Ch3 id=\"vollständiges-memory-dump\">Vollständiges Memory Dump\u003C/h3>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"bash\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># Partitionslayout ermitteln\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">adb\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> shell\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> su\u003C/span>\u003Cspan style=\"color:#79B8FF\"> -c\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> \"cat /proc/partitions\"\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">adb\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> shell\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> su\u003C/span>\u003Cspan style=\"color:#79B8FF\"> -c\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> \"ls -la /dev/block/\"\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># Vollständiges Device Image (Root erforderlich)\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">adb\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> shell\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> su\u003C/span>\u003Cspan style=\"color:#79B8FF\"> -c\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> \"dd if=/dev/block/mmcblk0 of=/sdcard/full_device.img bs=4096\"\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">adb\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> pull\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> /sdcard/full_device.img\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Ch1 id=\"best-practices\">Best Practices\u003C/h1>\n\u003Ch2 id=\"rechtliche-compliance\">Rechtliche Compliance\u003C/h2>\n\u003Ch3 id=\"dokumentation-und-chain-of-custody\">Dokumentation und Chain of Custody\u003C/h3>\n\u003Cul>\n\u003Cli>\u003Cstrong>Vollständige Dokumentation\u003C/strong>: Wer, Was, Wann, Wo, Warum\u003C/li>\n\u003Cli>\u003Cstrong>Hash-Verifikation\u003C/strong>: MD5/SHA-256 für alle extrahierten Daten\u003C/li>\n\u003Cli>\u003Cstrong>Nur forensische Kopien analysieren\u003C/strong>, niemals Originaldaten\u003C/li>\n\u003Cli>\u003Cstrong>Schriftliche Genehmigung\u003C/strong> für Geräteanalyse einholen\u003C/li>\n\u003C/ul>\n\u003Ch3 id=\"familiengeräte-und-nachlässe\">Familiengeräte und Nachlässe\u003C/h3>\n\u003Cul>\n\u003Cli>Genehmigung durch Nachlassverwalter erforderlich\u003C/li>\n\u003Cli>Gerichtsbeschlüsse für Cloud-Zugang eventuell nötig\u003C/li>\n\u003Cli>Drittpartei-Kommunikation kann weiterhin geschützt sein\u003C/li>\n\u003C/ul>\n\u003Ch2 id=\"technische-best-practices\">Technische Best Practices\u003C/h2>\n\u003Ch3 id=\"hash-integrität-sicherstellen\">Hash-Integrität sicherstellen\u003C/h3>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"bash\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># Hash vor und nach Transfer prüfen\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">md5sum\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> original_file.db\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">sha256sum\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> original_file.db\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># Hash-Verifikation dokumentieren\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#79B8FF\">echo\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> \"$(\u003C/span>\u003Cspan style=\"color:#B392F0\">date\u003C/span>\u003Cspan style=\"color:#9ECBFF\">): MD5: $(\u003C/span>\u003Cspan style=\"color:#B392F0\">md5sum\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> file.db)\"\u003C/span>\u003Cspan style=\"color:#F97583\"> >>\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> chain_of_custody.log\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Ch3 id=\"sichere-arbeitsumgebung\">Sichere Arbeitsumgebung\u003C/h3>\n\u003Cul>\n\u003Cli>Isolierte VM für Forensik-Arbeit\u003C/li>\n\u003Cli>Netzwerk-Isolation während Analyse\u003C/li>\n\u003Cli>Verschlüsselte Speicherung aller Evidenz\u003C/li>\n\u003Cli>Regelmäßige Backups der Case-Datenbanken\u003C/li>\n\u003C/ul>\n\u003Ch3 id=\"qualitätssicherung\">Qualitätssicherung\u003C/h3>\n\u003Cul>\n\u003Cli>Peer-Review kritischer Analysen\u003C/li>\n\u003Cli>Standardisierte Arbeitsabläufe (SOPs)\u003C/li>\n\u003Cli>Regelmäßige Tool-Validierung\u003C/li>\n\u003Cli>Kontinuierliche Weiterbildung\u003C/li>\n\u003C/ul>\n\u003Ch2 id=\"erfolgsmaximierung-nach-gerätehersteller\">Erfolgsmaximierung nach Gerätehersteller\u003C/h2>\n\u003Ch3 id=\"mediatek-geräte-höchste-erfolgsrate\">MediaTek-Geräte (Höchste Erfolgsrate)\u003C/h3>\n\u003Cul>\n\u003Cli>BootROM-Exploits für MT6735, MT6737, MT6750, MT6753, MT6797\u003C/li>\n\u003Cli>MTKClient für Hardware-Level-Zugang\u003C/li>\n\u003Cli>Erfolgsrate: 80%+ für Geräte 2015-2019\u003C/li>\n\u003C/ul>\n\u003Ch3 id=\"samsung-geräte\">Samsung-Geräte\u003C/h3>\n\u003Cul>\n\u003Cli>Ältere Knox-Implementierungen umgehbar\u003C/li>\n\u003Cli>Emergency Dialer Exploits für Android 4.x\u003C/li>\n\u003Cli>Erfolgsrate: 40-70% je nach Knox-Version\u003C/li>\n\u003C/ul>\n\u003Ch3 id=\"pixelnexus-geräte\">Pixel/Nexus-Geräte\u003C/h3>\n\u003Cul>\n\u003Cli>Bootloader-Unlocking oft möglich\u003C/li>\n\u003Cli>Fastboot-basierte Recovery-Installation\u003C/li>\n\u003Cli>Erfolgsrate: 60-80% bei freigeschaltetem Bootloader\u003C/li>\n\u003C/ul>\n\u003Ch1 id=\"troubleshooting\">Troubleshooting\u003C/h1>\n\u003Ch2 id=\"problem-adb-erkennt-gerät-nicht\">Problem: ADB erkennt Gerät nicht\u003C/h2>\n\u003Ch3 id=\"lösung-usb-treiber-und-berechtigungen\">Lösung: USB-Treiber und Berechtigungen\u003C/h3>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"bash\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># Linux: USB-Berechtigungen prüfen\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">lsusb\u003C/span>\u003Cspan style=\"color:#F97583\"> |\u003C/span>\u003Cspan style=\"color:#B392F0\"> grep\u003C/span>\u003Cspan style=\"color:#79B8FF\"> -i\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> android\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">sudo\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> chmod\u003C/span>\u003Cspan style=\"color:#79B8FF\"> 666\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> /dev/bus/usb/XXX/XXX\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># udev-Regeln erstellen\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#79B8FF\">echo\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> 'SUBSYSTEM==\"usb\", ATTR{idVendor}==\"18d1\", MODE=\"0666\", GROUP=\"plugdev\"'\u003C/span>\u003Cspan style=\"color:#F97583\"> |\u003C/span>\u003Cspan style=\"color:#B392F0\"> sudo\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> tee\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> /etc/udev/rules.d/51-android.rules\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">sudo\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> udevadm\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> control\u003C/span>\u003Cspan style=\"color:#79B8FF\"> --reload-rules\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Ch3 id=\"windows-treiber-installation\">Windows: Treiber-Installation\u003C/h3>\n\u003Col>\n\u003Cli>Geräte-Manager öffnen\u003C/li>\n\u003Cli>Android-Gerät mit Warnsymbol finden\u003C/li>\n\u003Cli>Treiber manuell installieren (Android USB Driver)\u003C/li>\n\u003C/ol>\n\u003Ch2 id=\"problem-verschlüsselte-android-backups\">Problem: Verschlüsselte Android Backups\u003C/h2>\n\u003Ch3 id=\"lösung-android-backup-extractor\">Lösung: Android Backup Extractor\u003C/h3>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"bash\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># ADB Backup Extractor installieren\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">git\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> clone\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> https://github.com/nelenkov/android-backup-extractor.git\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#79B8FF\">cd\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> android-backup-extractor\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">gradle\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> build\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># Backup entschlüsseln\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">java\u003C/span>\u003Cspan style=\"color:#79B8FF\"> -jar\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> abe.jar\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> unpack\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> backup.ab\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> backup.tar\u003C/span>\u003Cspan style=\"color:#E1E4E8\"> [password]\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Ch2 id=\"problem-unzureichende-berechtigungen-für-datenextraktion\">Problem: Unzureichende Berechtigungen für Datenextraktion\u003C/h2>\n\u003Ch3 id=\"lösung-alternative-extraktionsmethoden\">Lösung: Alternative Extraktionsmethoden\u003C/h3>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"bash\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># AFLogical OSE für begrenzte Extraktion ohne Root\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># WhatsApp Key/DB Extractor für spezifische Apps\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># Backup-basierte Extraktion als Fallback\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># Custom Recovery für erweiterten Zugang\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">fastboot\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> flash\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> recovery\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> twrp-device.img\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Ch2 id=\"problem-aleapp-parsing-fehler\">Problem: ALEAPP Parsing-Fehler\u003C/h2>\n\u003Ch3 id=\"lösung-datenformat-probleme-beheben\">Lösung: Datenformat-Probleme beheben\u003C/h3>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"bash\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># Log-Dateien prüfen\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">python3\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> aleapp.py\u003C/span>\u003Cspan style=\"color:#79B8FF\"> -t\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> dir\u003C/span>\u003Cspan style=\"color:#79B8FF\"> -i\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> /path/to/data\u003C/span>\u003Cspan style=\"color:#79B8FF\"> -o\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> output\u003C/span>\u003Cspan style=\"color:#79B8FF\"> --debug\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># Spezifische Parser deaktivieren\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># Manuelle SQLite-Analyse bei Parser-Fehlern\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">sqlite3\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> database.db\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> \".tables\"\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">sqlite3\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> database.db\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> \".schema table_name\"\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Ch1 id=\"erweiterte-techniken\">Erweiterte Techniken\u003C/h1>\n\u003Ch2 id=\"memory-forensics-mit-lime\">Memory Forensics mit LiME\u003C/h2>\n\u003Ch3 id=\"lime-für-arm-devices-kompilieren\">LiME für ARM-Devices kompilieren\u003C/h3>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"bash\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># Cross-Compilation Setup\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#F97583\">export\u003C/span>\u003Cspan style=\"color:#E1E4E8\"> ARCH\u003C/span>\u003Cspan style=\"color:#F97583\">=\u003C/span>\u003Cspan style=\"color:#E1E4E8\">arm\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#F97583\">export\u003C/span>\u003Cspan style=\"color:#E1E4E8\"> CROSS_COMPILE\u003C/span>\u003Cspan style=\"color:#F97583\">=\u003C/span>\u003Cspan style=\"color:#E1E4E8\">arm-linux-gnueabi-\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#F97583\">export\u003C/span>\u003Cspan style=\"color:#E1E4E8\"> KERNEL_DIR\u003C/span>\u003Cspan style=\"color:#F97583\">=\u003C/span>\u003Cspan style=\"color:#E1E4E8\">/path/to/kernel/source\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># LiME Module kompilieren\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">git\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> clone\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> https://github.com/504ensicsLabs/LiME.git\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#79B8FF\">cd\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> LiME/src\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">make\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># Memory Dump erstellen (Root erforderlich)\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">adb\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> push\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> lime.ko\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> /data/local/tmp/\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">adb\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> shell\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> su\u003C/span>\u003Cspan style=\"color:#79B8FF\"> -c\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> \"insmod /data/local/tmp/lime.ko 'path=/sdcard/memory.lime format=lime'\"\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Ch3 id=\"volatility-analyse-von-android-memory\">Volatility-Analyse von Android Memory\u003C/h3>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"bash\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># Memory Dump analysieren\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">python\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> vol.py\u003C/span>\u003Cspan style=\"color:#79B8FF\"> -f\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> memory.lime\u003C/span>\u003Cspan style=\"color:#79B8FF\"> --profile=Linux\u003C/span>\u003Cspan style=\"color:#F97583\"> <\u003C/span>\u003Cspan style=\"color:#9ECBFF\">profil\u003C/span>\u003Cspan style=\"color:#E1E4E8\">e\u003C/span>\u003Cspan style=\"color:#F97583\">>\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> linux.pslist\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">python\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> vol.py\u003C/span>\u003Cspan style=\"color:#79B8FF\"> -f\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> memory.lime\u003C/span>\u003Cspan style=\"color:#79B8FF\"> --profile=Linux\u003C/span>\u003Cspan style=\"color:#F97583\"> <\u003C/span>\u003Cspan style=\"color:#9ECBFF\">profil\u003C/span>\u003Cspan style=\"color:#E1E4E8\">e\u003C/span>\u003Cspan style=\"color:#F97583\">>\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> linux.bash\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">python\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> vol.py\u003C/span>\u003Cspan style=\"color:#79B8FF\"> -f\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> memory.lime\u003C/span>\u003Cspan style=\"color:#79B8FF\"> --profile=Linux\u003C/span>\u003Cspan style=\"color:#F97583\"> <\u003C/span>\u003Cspan style=\"color:#9ECBFF\">profil\u003C/span>\u003Cspan style=\"color:#E1E4E8\">e\u003C/span>\u003Cspan style=\"color:#F97583\">>\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> linux.netstat\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Ch2 id=\"frida-basierte-runtime-analyse\">FRIDA-basierte Runtime-Analyse\u003C/h2>\n\u003Ch3 id=\"frida-für-kryptographie-hooks\">FRIDA für Kryptographie-Hooks\u003C/h3>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"javascript\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\">// crypto_hooks.js - SSL/TLS Traffic abfangen\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#E1E4E8\">Java.\u003C/span>\u003Cspan style=\"color:#B392F0\">perform\u003C/span>\u003Cspan style=\"color:#E1E4E8\">(\u003C/span>\u003Cspan style=\"color:#F97583\">function\u003C/span>\u003Cspan style=\"color:#E1E4E8\">() {\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#F97583\">    var\u003C/span>\u003Cspan style=\"color:#E1E4E8\"> SSLContext \u003C/span>\u003Cspan style=\"color:#F97583\">=\u003C/span>\u003Cspan style=\"color:#E1E4E8\"> Java.\u003C/span>\u003Cspan style=\"color:#B392F0\">use\u003C/span>\u003Cspan style=\"color:#E1E4E8\">(\u003C/span>\u003Cspan style=\"color:#9ECBFF\">\"javax.net.ssl.SSLContext\"\u003C/span>\u003Cspan style=\"color:#E1E4E8\">);\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#E1E4E8\">    SSLContext.init.\u003C/span>\u003Cspan style=\"color:#B392F0\">overload\u003C/span>\u003Cspan style=\"color:#E1E4E8\">(\u003C/span>\u003Cspan style=\"color:#9ECBFF\">'[Ljavax.net.ssl.KeyManager;'\u003C/span>\u003Cspan style=\"color:#E1E4E8\">, \u003C/span>\u003Cspan style=\"color:#9ECBFF\">'[Ljavax.net.ssl.TrustManager;'\u003C/span>\u003Cspan style=\"color:#E1E4E8\">, \u003C/span>\u003Cspan style=\"color:#9ECBFF\">'java.security.SecureRandom'\u003C/span>\u003Cspan style=\"color:#E1E4E8\">).\u003C/span>\u003Cspan style=\"color:#B392F0\">implementation\u003C/span>\u003Cspan style=\"color:#F97583\"> =\u003C/span>\u003Cspan style=\"color:#F97583\"> function\u003C/span>\u003Cspan style=\"color:#E1E4E8\">(\u003C/span>\u003Cspan style=\"color:#FFAB70\">keyManagers\u003C/span>\u003Cspan style=\"color:#E1E4E8\">, \u003C/span>\u003Cspan style=\"color:#FFAB70\">trustManagers\u003C/span>\u003Cspan style=\"color:#E1E4E8\">, \u003C/span>\u003Cspan style=\"color:#FFAB70\">secureRandom\u003C/span>\u003Cspan style=\"color:#E1E4E8\">) {\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#E1E4E8\">        console.\u003C/span>\u003Cspan style=\"color:#B392F0\">log\u003C/span>\u003Cspan style=\"color:#E1E4E8\">(\u003C/span>\u003Cspan style=\"color:#9ECBFF\">\"[+] SSLContext.init() called\"\u003C/span>\u003Cspan style=\"color:#E1E4E8\">);\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#79B8FF\">        this\u003C/span>\u003Cspan style=\"color:#E1E4E8\">.\u003C/span>\u003Cspan style=\"color:#B392F0\">init\u003C/span>\u003Cspan style=\"color:#E1E4E8\">(keyManagers, trustManagers, secureRandom);\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#E1E4E8\">    };\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#E1E4E8\">});\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Ch3 id=\"frida-installation-und-verwendung\">FRIDA Installation und Verwendung\u003C/h3>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"bash\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># FRIDA Server auf Android-Gerät installieren\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">adb\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> push\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> frida-server\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> /data/local/tmp/\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">adb\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> shell\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> su\u003C/span>\u003Cspan style=\"color:#79B8FF\"> -c\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> \"chmod 755 /data/local/tmp/frida-server\"\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">adb\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> shell\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> su\u003C/span>\u003Cspan style=\"color:#79B8FF\"> -c\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> \"/data/local/tmp/frida-server &\"\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># Script ausführen\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">frida\u003C/span>\u003Cspan style=\"color:#79B8FF\"> -U\u003C/span>\u003Cspan style=\"color:#79B8FF\"> -l\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> crypto_hooks.js\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> com.target.package\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Ch2 id=\"custom-recovery-und-fastboot-exploits\">Custom Recovery und Fastboot-Exploits\u003C/h2>\n\u003Ch3 id=\"twrp-installation-für-forensischen-zugang\">TWRP Installation für forensischen Zugang\u003C/h3>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"bash\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># Bootloader entsperren (Herstellerabhängig)\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">fastboot\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> oem\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> unlock\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># Oder\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">fastboot\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> flashing\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> unlock\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># TWRP flashen\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">fastboot\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> flash\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> recovery\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> twrp-device.img\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">fastboot\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> boot\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> twrp-device.img\u003C/span>\u003Cspan style=\"color:#6A737D\">  # Temporäre Installation\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># In TWRP: ADB-Zugang mit Root-Berechtigungen\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">adb\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> shell\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> mount\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> /system\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">adb\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> shell\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> mount\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> /data\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Ch3 id=\"partitions-imaging-mit-dd\">Partitions-Imaging mit dd\u003C/h3>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"bash\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># Vollständige Partition-Liste\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">adb\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> shell\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> cat\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> /proc/partitions\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># Kritische Partitionen extrahieren\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">adb\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> shell\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> dd\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> if=/dev/block/bootdevice/by-name/system\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> of=/external_sd/system.img\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">adb\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> shell\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> dd\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> if=/dev/block/bootdevice/by-name/userdata\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> of=/external_sd/userdata.img\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">adb\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> shell\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> dd\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> if=/dev/block/bootdevice/by-name/boot\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> of=/external_sd/boot.img\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Ch2 id=\"sqlite-forensics-und-gelöschte-daten\">SQLite Forensics und gelöschte Daten\u003C/h2>\n\u003Ch3 id=\"erweiterte-sqlite-analyse\">Erweiterte SQLite-Analyse\u003C/h3>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"bash\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># Freelist-Analyse für gelöschte Einträge\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">sqlite3\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> database.db\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> \"PRAGMA freelist_count;\"\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">sqlite3\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> database.db\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> \"PRAGMA page_size;\"\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># WAL-Datei Analyse\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">sqlite3\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> database.db\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> \"PRAGMA wal_checkpoint;\"\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">strings\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> database.db-wal\u003C/span>\u003Cspan style=\"color:#F97583\"> |\u003C/span>\u003Cspan style=\"color:#B392F0\"> grep\u003C/span>\u003Cspan style=\"color:#79B8FF\"> -i\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> \"search_term\"\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># Undark für Deleted Record Recovery\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">undark\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> database.db\u003C/span>\u003Cspan style=\"color:#79B8FF\"> --freelist\u003C/span>\u003Cspan style=\"color:#79B8FF\"> --export-csv\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Ch3 id=\"timeline-rekonstruktion\">Timeline-Rekonstruktion\u003C/h3>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"bash\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># Autopsy Timeline-Generierung\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># Tools → Generate Timeline\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># Analyse von MAC-Times (Modified, Accessed, Created)\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># Plaso Timeline-Tools\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">log2timeline.py\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> timeline.plaso\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> /path/to/android/data/\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">psort.py\u003C/span>\u003Cspan style=\"color:#79B8FF\"> -o\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> dynamic\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> timeline.plaso\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Ch2 id=\"weiterführende-ressourcen\">Weiterführende Ressourcen\u003C/h2>\n\u003Ch3 id=\"dokumentation-und-standards\">Dokumentation und Standards\u003C/h3>\n\u003Cul>\n\u003Cli>\u003Ca href=\"https://csrc.nist.gov/pubs/sp/800/101/r1/final\">NIST SP 800-101 Rev. 1 - Mobile Device Forensics Guidelines\u003C/a>\u003C/li>\n\u003Cli>\u003Ca href=\"https://www.sans.org/cyber-security-courses/advanced-smartphone-mobile-device-forensics/\">SANS FOR585 - Smartphone Forensics\u003C/a>\u003C/li>\n\u003Cli>\u003Ca href=\"https://github.com/abrignoni/ALEAPP\">ALEAPP GitHub Repository\u003C/a>\u003C/li>\n\u003Cli>\u003Ca href=\"https://docs.mvt.re/en/latest/\">MVT Documentation\u003C/a>\u003C/li>\n\u003C/ul>\n\u003Ch3 id=\"community-und-weiterbildung\">Community und Weiterbildung\u003C/h3>\n\u003Cul>\n\u003Cli>\u003Ca href=\"https://sleuthkit.org/autopsy/docs/\">Autopsy User Documentation\u003C/a>\u003C/li>\n\u003Cli>\u003Ca href=\"https://github.com/impillar/AndroidReferences/blob/master/AndroidTools.md\">Android Forensics References\u003C/a>\u003C/li>\n\u003Cli>\u003Ca href=\"https://github.com/mesquidar/ForensicsTools\">Digital Forensics Framework Collection\u003C/a>\u003C/li>\n\u003C/ul>\n\u003Ch3 id=\"spezialisierte-tools\">Spezialisierte Tools\u003C/h3>\n\u003Cul>\n\u003Cli>\u003Ca href=\"https://github.com/bkerler/mtkclient\">MTKClient für MediaTek Exploits\u003C/a>\u003C/li>\n\u003Cli>\u003Ca href=\"https://github.com/nowsecure/android-forensics\">Android Forensics Framework\u003C/a>\u003C/li>\n\u003Cli>\u003Ca href=\"https://santoku-linux.com/\">Santoku Linux Mobile Forensics Distribution\u003C/a>\u003C/li>\n\u003C/ul>\n\u003Chr>\n\u003Cp>\u003Cstrong>Wichtiger Hinweis\u003C/strong>: Diese Anleitung dient ausschließlich für autorisierte forensische Untersuchungen. Stellen Sie sicher, dass Sie über entsprechende rechtliche Befugnisse verfügen, bevor Sie diese Techniken anwenden. Bei Zweifeln konsultieren Sie Rechtsberatung.\u003C/p>",{"headings":309,"localImagePaths":531,"remoteImagePaths":532,"frontmatter":290,"imagePaths":533},[310,311,314,317,318,321,324,326,329,332,335,338,341,344,347,350,351,354,357,360,363,366,369,372,375,378,379,382,385,388,391,394,397,400,403,406,409,412,415,418,419,422,425,428,431,434,437,440,443,446,449,452,453,456,459,462,465,468,471,474,477,480,483,486,489,492,495,498,501,504,507,510,513,516,519,522,525,528],{"depth":43,"slug":44,"text":45},{"depth":47,"slug":312,"text":313},"kernkomponenten-des-open-source-forensik-stacks","Kernkomponenten des Open-Source Forensik-Stacks",{"depth":47,"slug":315,"text":316},"erfolgsraten-nach-gerätealter","Erfolgsraten nach Gerätealter",{"depth":43,"slug":48,"text":49},{"depth":47,"slug":319,"text":320},"sift-workstation-setup","SIFT Workstation Setup",{"depth":51,"slug":322,"text":323},"systemanforderungen","Systemanforderungen",{"depth":51,"slug":325,"text":49},"installation-1",{"depth":47,"slug":327,"text":328},"autopsy-installation","Autopsy Installation",{"depth":51,"slug":330,"text":331},"windows-installation","Windows Installation",{"depth":51,"slug":333,"text":334},"linux-installation","Linux Installation",{"depth":47,"slug":336,"text":337},"essential-tools-installation","Essential Tools Installation",{"depth":51,"slug":339,"text":340},"android-debug-bridge-adb","Android Debug Bridge (ADB)",{"depth":51,"slug":342,"text":343},"aleapp-installation","ALEAPP Installation",{"depth":51,"slug":345,"text":346},"mobile-verification-toolkit-mvt","Mobile Verification Toolkit (MVT)",{"depth":51,"slug":348,"text":349},"andriller-installation","Andriller Installation",{"depth":43,"slug":58,"text":59},{"depth":47,"slug":352,"text":353},"adb-setup-und-gerätevorbereitung","ADB Setup und Gerätevorbereitung",{"depth":51,"slug":355,"text":356},"usb-debugging-aktivieren","USB-Debugging aktivieren",{"depth":51,"slug":358,"text":359},"adb-verbindung-testen","ADB Verbindung testen",{"depth":47,"slug":361,"text":362},"autopsy-projektkonfiguration","Autopsy Projektkonfiguration",{"depth":51,"slug":364,"text":365},"case-setup","Case-Setup",{"depth":51,"slug":367,"text":368},"android-analyzer-module-aktivieren","Android Analyzer Module aktivieren",{"depth":51,"slug":370,"text":371},"hash-algorithmen-konfigurieren","Hash-Algorithmen konfigurieren",{"depth":47,"slug":373,"text":374},"mvt-konfiguration","MVT Konfiguration",{"depth":51,"slug":376,"text":377},"konfigurationsdatei-erstellen","Konfigurationsdatei erstellen",{"depth":43,"slug":70,"text":71},{"depth":47,"slug":380,"text":381},"fall-1-logische-datenextraktion-mit-adb","Fall 1: Logische Datenextraktion mit ADB",{"depth":51,"slug":383,"text":384},"geräteinformationen-sammeln","Geräteinformationen sammeln",{"depth":51,"slug":386,"text":387},"datenbank-extraktion","Datenbank-Extraktion",{"depth":51,"slug":389,"text":390},"whatsapp-datenextraktion","WhatsApp Datenextraktion",{"depth":47,"slug":392,"text":393},"fall-2-android-backup-analyse","Fall 2: Android Backup-Analyse",{"depth":51,"slug":395,"text":396},"vollständiges-backup-erstellen","Vollständiges Backup erstellen",{"depth":51,"slug":398,"text":399},"backup-mit-aleapp-analysieren","Backup mit ALEAPP analysieren",{"depth":47,"slug":401,"text":402},"fall-3-mvt-kompromittierungsanalyse","Fall 3: MVT Kompromittierungsanalyse",{"depth":51,"slug":404,"text":405},"live-geräteanalyse","Live-Geräteanalyse",{"depth":51,"slug":407,"text":408},"ioc-suche-mit-pegasus-indikatoren","IOC-Suche mit Pegasus-Indikatoren",{"depth":47,"slug":410,"text":411},"fall-4-physische-extraktion-root-erforderlich","Fall 4: Physische Extraktion (Root erforderlich)",{"depth":51,"slug":413,"text":414},"device-rooting---mediatek-geräte","Device Rooting - MediaTek Geräte",{"depth":51,"slug":416,"text":417},"vollständiges-memory-dump","Vollständiges Memory Dump",{"depth":43,"slug":79,"text":80},{"depth":47,"slug":420,"text":421},"rechtliche-compliance","Rechtliche Compliance",{"depth":51,"slug":423,"text":424},"dokumentation-und-chain-of-custody","Dokumentation und Chain of Custody",{"depth":51,"slug":426,"text":427},"familiengeräte-und-nachlässe","Familiengeräte und Nachlässe",{"depth":47,"slug":429,"text":430},"technische-best-practices","Technische Best Practices",{"depth":51,"slug":432,"text":433},"hash-integrität-sicherstellen","Hash-Integrität sicherstellen",{"depth":51,"slug":435,"text":436},"sichere-arbeitsumgebung","Sichere Arbeitsumgebung",{"depth":51,"slug":438,"text":439},"qualitätssicherung","Qualitätssicherung",{"depth":47,"slug":441,"text":442},"erfolgsmaximierung-nach-gerätehersteller","Erfolgsmaximierung nach Gerätehersteller",{"depth":51,"slug":444,"text":445},"mediatek-geräte-höchste-erfolgsrate","MediaTek-Geräte (Höchste Erfolgsrate)",{"depth":51,"slug":447,"text":448},"samsung-geräte","Samsung-Geräte",{"depth":51,"slug":450,"text":451},"pixelnexus-geräte","Pixel/Nexus-Geräte",{"depth":43,"slug":82,"text":83},{"depth":47,"slug":454,"text":455},"problem-adb-erkennt-gerät-nicht","Problem: ADB erkennt Gerät nicht",{"depth":51,"slug":457,"text":458},"lösung-usb-treiber-und-berechtigungen","Lösung: USB-Treiber und Berechtigungen",{"depth":51,"slug":460,"text":461},"windows-treiber-installation","Windows: Treiber-Installation",{"depth":47,"slug":463,"text":464},"problem-verschlüsselte-android-backups","Problem: Verschlüsselte Android Backups",{"depth":51,"slug":466,"text":467},"lösung-android-backup-extractor","Lösung: Android Backup Extractor",{"depth":47,"slug":469,"text":470},"problem-unzureichende-berechtigungen-für-datenextraktion","Problem: Unzureichende Berechtigungen für Datenextraktion",{"depth":51,"slug":472,"text":473},"lösung-alternative-extraktionsmethoden","Lösung: Alternative Extraktionsmethoden",{"depth":47,"slug":475,"text":476},"problem-aleapp-parsing-fehler","Problem: ALEAPP Parsing-Fehler",{"depth":51,"slug":478,"text":479},"lösung-datenformat-probleme-beheben","Lösung: Datenformat-Probleme beheben",{"depth":43,"slug":481,"text":482},"erweiterte-techniken","Erweiterte Techniken",{"depth":47,"slug":484,"text":485},"memory-forensics-mit-lime","Memory Forensics mit LiME",{"depth":51,"slug":487,"text":488},"lime-für-arm-devices-kompilieren","LiME für ARM-Devices kompilieren",{"depth":51,"slug":490,"text":491},"volatility-analyse-von-android-memory","Volatility-Analyse von Android Memory",{"depth":47,"slug":493,"text":494},"frida-basierte-runtime-analyse","FRIDA-basierte Runtime-Analyse",{"depth":51,"slug":496,"text":497},"frida-für-kryptographie-hooks","FRIDA für Kryptographie-Hooks",{"depth":51,"slug":499,"text":500},"frida-installation-und-verwendung","FRIDA Installation und Verwendung",{"depth":47,"slug":502,"text":503},"custom-recovery-und-fastboot-exploits","Custom Recovery und Fastboot-Exploits",{"depth":51,"slug":505,"text":506},"twrp-installation-für-forensischen-zugang","TWRP Installation für forensischen Zugang",{"depth":51,"slug":508,"text":509},"partitions-imaging-mit-dd","Partitions-Imaging mit dd",{"depth":47,"slug":511,"text":512},"sqlite-forensics-und-gelöschte-daten","SQLite Forensics und gelöschte Daten",{"depth":51,"slug":514,"text":515},"erweiterte-sqlite-analyse","Erweiterte SQLite-Analyse",{"depth":51,"slug":517,"text":518},"timeline-rekonstruktion","Timeline-Rekonstruktion",{"depth":47,"slug":520,"text":521},"weiterführende-ressourcen","Weiterführende Ressourcen",{"depth":51,"slug":523,"text":524},"dokumentation-und-standards","Dokumentation und Standards",{"depth":51,"slug":526,"text":527},"community-und-weiterbildung","Community und Weiterbildung",{"depth":51,"slug":529,"text":530},"spezialisierte-tools","Spezialisierte Tools",[],[],[],"android-logical-imaging.md","nextcloud",{"id":535,"data":537,"body":551,"filePath":552,"digest":553,"rendered":554,"legacyId":590},{"title":538,"tool_name":539,"description":540,"last_updated":541,"author":16,"difficulty":542,"categories":543,"tags":545,"sections":550,"review_status":34},"Nextcloud - Sichere Kollaborationsplattform","Nextcloud","Detaillierte Anleitung und Best Practices für Nextcloud in forensischen Einsatzszenarien",["Date","2025-07-20T00:00:00.000Z"],"novice",[544],"collaboration-general",[25,546,547,27,548,549],"collaboration","file-sharing","encryption","document-management",{"overview":32,"installation":32,"configuration":32,"usage_examples":32,"best_practices":32,"troubleshooting":32,"advanced_topics":33},"> **⚠️ Hinweis**: Dies ist ein vorläufiger, KI-generierter Knowledgebase-Eintrag. Wir freuen uns über Verbesserungen und Ergänzungen durch die Community!\n\n\n# Übersicht\n\nNextcloud ist eine Open-Source-Cloud-Suite, die speziell für die sichere Zusammenarbeit entwickelt wurde. Sie eignet sich ideal für forensische Teams, da sie eine DSGVO-konforme Umgebung mit verschlüsselter Dateiablage, Office-Integration und Videokonferenzen bereitstellt. Zusätzlich bietet Nextcloud einen integrierten SSO-Provider, der das Identitätsmanagement für andere forensische Tools stark vereinfacht.\n\nSkalierbar von kleinen Raspberry-Pi-Installationen bis hin zu hochverfügbaren Multi-Node-Setups.\n\n- **Website:** [nextcloud.com](https://nextcloud.com/)\n- **Demo/Projektinstanz:** [cloud.cc24.dev](https://cloud.cc24.dev)\n- **Statusseite:** [Mikoshi Status](https://status.mikoshi.de/api/badge/11/status)\n- **Lizenz:** AGPL-3.0\n\n---\n\n## Installation\n\n### Voraussetzungen\n\n- Linux-Server oder Raspberry Pi\n- PHP 8.1 oder höher\n- MariaDB/PostgreSQL\n- Webserver (Apache/Nginx)\n- SSL-Zertifikat (empfohlen: Let's Encrypt)\n\n### Installationsschritte (Ubuntu Beispiel)\n\n```bash\nsudo apt update && sudo apt upgrade\nsudo apt install apache2 mariadb-server libapache2-mod-php php php-mysql \\\n  php-gd php-xml php-mbstring php-curl php-zip php-intl php-bcmath unzip\n\nwget https://download.nextcloud.com/server/releases/latest.zip\nunzip latest.zip -d /var/www/\nchown -R www-data:www-data /var/www/nextcloud\n````\n\nDanach den Web-Installer im Browser aufrufen (`https://\u003Cyour-domain>/nextcloud`) und Setup abschließen.\n\n## Konfiguration\n\n* **Trusted Domains** in `config.php` definieren\n* SSO mit OpenID Connect aktivieren\n* Dateiverschlüsselung aktivieren (`Settings → Security`)\n* Benutzer und Gruppen über LDAP oder SAML integrieren\n\n## Verwendungsbeispiele\n\n### Gemeinsame Fallbearbeitung\n\n1. Ermittlungsordner als geteiltes Gruppenverzeichnis anlegen\n2. Versionierung und Kommentare zu forensischen Berichten aktivieren\n3. Vorschau für Office-Dateien, PDFs und Bilder direkt im Browser nutzen\n\n### Videokonferenzen mit \"Nextcloud Talk\"\n\n* Sichere Kommunikation zwischen Ermittlern und Sachverständigen\n* Ende-zu-Ende-verschlüsselt\n* Bildschirmfreigabe möglich\n\n### Automatischer Dateiimport per API\n\n* REST-Schnittstelle nutzen, um z. B. automatisch Logdateien oder Exportdaten hochzuladen\n* Ideal für Anbindung an SIEM, DLP oder Analyse-Pipelines\n\n## Best Practices\n\n* Zwei-Faktor-Authentifizierung aktivieren\n* Tägliche Backups der Datenbank und Datenstruktur\n* Nutzung von OnlyOffice oder Collabora für revisionssichere Dokumentenbearbeitung\n* Zugriff regelmäßig überprüfen, insbesondere bei externen Partnern\n\n## Troubleshooting\n\n### Problem: Langsame Performance\n\n**Lösung:** APCu aktivieren und Caching optimieren (`config.php → 'memcache.local'`).\n\n### Problem: Dateien erscheinen nicht im Sync\n\n**Lösung:** Cronjob für `files:scan` konfigurieren oder manuell ausführen:\n\n```bash\nsudo -u www-data php /var/www/nextcloud/occ files:scan --all\n```\n\n### Problem: Fehlermeldung \"Trusted domain not set\"\n\n**Lösung:** In `config/config.php` Eintrag `trusted_domains` korrekt konfigurieren:\n\n```php\n'trusted_domains' =>\n  array (\n    0 => 'yourdomain.tld',\n    1 => 'cloud.cc24.dev',\n  ),\n```\n\n## Weiterführende Themen\n\n* **Integration mit Forensik-Plattformen** (über WebDAV, API oder SSO)\n* **Custom Apps entwickeln** für spezielle Ermittlungs-Workflows\n* **Auditing aktivieren**: Nutzung und Änderungen nachvollziehen mit Protokollierungsfunktionen","src/content/knowledgebase/nextcloud.md","9294074e6083e37b",{"html":555,"metadata":556},"\u003Cblockquote>\n\u003Cp>\u003Cstrong>⚠️ Hinweis\u003C/strong>: Dies ist ein vorläufiger, KI-generierter Knowledgebase-Eintrag. Wir freuen uns über Verbesserungen und Ergänzungen durch die Community!\u003C/p>\n\u003C/blockquote>\n\u003Ch1 id=\"übersicht\">Übersicht\u003C/h1>\n\u003Cp>Nextcloud ist eine Open-Source-Cloud-Suite, die speziell für die sichere Zusammenarbeit entwickelt wurde. Sie eignet sich ideal für forensische Teams, da sie eine DSGVO-konforme Umgebung mit verschlüsselter Dateiablage, Office-Integration und Videokonferenzen bereitstellt. Zusätzlich bietet Nextcloud einen integrierten SSO-Provider, der das Identitätsmanagement für andere forensische Tools stark vereinfacht.\u003C/p>\n\u003Cp>Skalierbar von kleinen Raspberry-Pi-Installationen bis hin zu hochverfügbaren Multi-Node-Setups.\u003C/p>\n\u003Cul>\n\u003Cli>\u003Cstrong>Website:\u003C/strong> \u003Ca href=\"https://nextcloud.com/\">nextcloud.com\u003C/a>\u003C/li>\n\u003Cli>\u003Cstrong>Demo/Projektinstanz:\u003C/strong> \u003Ca href=\"https://cloud.cc24.dev\">cloud.cc24.dev\u003C/a>\u003C/li>\n\u003Cli>\u003Cstrong>Statusseite:\u003C/strong> \u003Ca href=\"https://status.mikoshi.de/api/badge/11/status\">Mikoshi Status\u003C/a>\u003C/li>\n\u003Cli>\u003Cstrong>Lizenz:\u003C/strong> AGPL-3.0\u003C/li>\n\u003C/ul>\n\u003Chr>\n\u003Ch2 id=\"installation\">Installation\u003C/h2>\n\u003Ch3 id=\"voraussetzungen\">Voraussetzungen\u003C/h3>\n\u003Cul>\n\u003Cli>Linux-Server oder Raspberry Pi\u003C/li>\n\u003Cli>PHP 8.1 oder höher\u003C/li>\n\u003Cli>MariaDB/PostgreSQL\u003C/li>\n\u003Cli>Webserver (Apache/Nginx)\u003C/li>\n\u003Cli>SSL-Zertifikat (empfohlen: Let’s Encrypt)\u003C/li>\n\u003C/ul>\n\u003Ch3 id=\"installationsschritte-ubuntu-beispiel\">Installationsschritte (Ubuntu Beispiel)\u003C/h3>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"bash\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">sudo\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> apt\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> update\u003C/span>\u003Cspan style=\"color:#E1E4E8\"> && \u003C/span>\u003Cspan style=\"color:#B392F0\">sudo\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> apt\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> upgrade\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">sudo\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> apt\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> install\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> apache2\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> mariadb-server\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> libapache2-mod-php\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> php\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> php-mysql\u003C/span>\u003Cspan style=\"color:#79B8FF\"> \\\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#9ECBFF\">  php-gd\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> php-xml\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> php-mbstring\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> php-curl\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> php-zip\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> php-intl\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> php-bcmath\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> unzip\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">wget\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> https://download.nextcloud.com/server/releases/latest.zip\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">unzip\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> latest.zip\u003C/span>\u003Cspan style=\"color:#79B8FF\"> -d\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> /var/www/\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">chown\u003C/span>\u003Cspan style=\"color:#79B8FF\"> -R\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> www-data:www-data\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> /var/www/nextcloud\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Cp>Danach den Web-Installer im Browser aufrufen (\u003Ccode>https://<your-domain>/nextcloud\u003C/code>) und Setup abschließen.\u003C/p>\n\u003Ch2 id=\"konfiguration\">Konfiguration\u003C/h2>\n\u003Cul>\n\u003Cli>\u003Cstrong>Trusted Domains\u003C/strong> in \u003Ccode>config.php\u003C/code> definieren\u003C/li>\n\u003Cli>SSO mit OpenID Connect aktivieren\u003C/li>\n\u003Cli>Dateiverschlüsselung aktivieren (\u003Ccode>Settings → Security\u003C/code>)\u003C/li>\n\u003Cli>Benutzer und Gruppen über LDAP oder SAML integrieren\u003C/li>\n\u003C/ul>\n\u003Ch2 id=\"verwendungsbeispiele\">Verwendungsbeispiele\u003C/h2>\n\u003Ch3 id=\"gemeinsame-fallbearbeitung\">Gemeinsame Fallbearbeitung\u003C/h3>\n\u003Col>\n\u003Cli>Ermittlungsordner als geteiltes Gruppenverzeichnis anlegen\u003C/li>\n\u003Cli>Versionierung und Kommentare zu forensischen Berichten aktivieren\u003C/li>\n\u003Cli>Vorschau für Office-Dateien, PDFs und Bilder direkt im Browser nutzen\u003C/li>\n\u003C/ol>\n\u003Ch3 id=\"videokonferenzen-mit-nextcloud-talk\">Videokonferenzen mit “Nextcloud Talk”\u003C/h3>\n\u003Cul>\n\u003Cli>Sichere Kommunikation zwischen Ermittlern und Sachverständigen\u003C/li>\n\u003Cli>Ende-zu-Ende-verschlüsselt\u003C/li>\n\u003Cli>Bildschirmfreigabe möglich\u003C/li>\n\u003C/ul>\n\u003Ch3 id=\"automatischer-dateiimport-per-api\">Automatischer Dateiimport per API\u003C/h3>\n\u003Cul>\n\u003Cli>REST-Schnittstelle nutzen, um z. B. automatisch Logdateien oder Exportdaten hochzuladen\u003C/li>\n\u003Cli>Ideal für Anbindung an SIEM, DLP oder Analyse-Pipelines\u003C/li>\n\u003C/ul>\n\u003Ch2 id=\"best-practices\">Best Practices\u003C/h2>\n\u003Cul>\n\u003Cli>Zwei-Faktor-Authentifizierung aktivieren\u003C/li>\n\u003Cli>Tägliche Backups der Datenbank und Datenstruktur\u003C/li>\n\u003Cli>Nutzung von OnlyOffice oder Collabora für revisionssichere Dokumentenbearbeitung\u003C/li>\n\u003Cli>Zugriff regelmäßig überprüfen, insbesondere bei externen Partnern\u003C/li>\n\u003C/ul>\n\u003Ch2 id=\"troubleshooting\">Troubleshooting\u003C/h2>\n\u003Ch3 id=\"problem-langsame-performance\">Problem: Langsame Performance\u003C/h3>\n\u003Cp>\u003Cstrong>Lösung:\u003C/strong> APCu aktivieren und Caching optimieren (\u003Ccode>config.php → 'memcache.local'\u003C/code>).\u003C/p>\n\u003Ch3 id=\"problem-dateien-erscheinen-nicht-im-sync\">Problem: Dateien erscheinen nicht im Sync\u003C/h3>\n\u003Cp>\u003Cstrong>Lösung:\u003C/strong> Cronjob für \u003Ccode>files:scan\u003C/code> konfigurieren oder manuell ausführen:\u003C/p>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"bash\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">sudo\u003C/span>\u003Cspan style=\"color:#79B8FF\"> -u\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> www-data\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> php\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> /var/www/nextcloud/occ\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> files:scan\u003C/span>\u003Cspan style=\"color:#79B8FF\"> --all\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Ch3 id=\"problem-fehlermeldung-trusted-domain-not-set\">Problem: Fehlermeldung “Trusted domain not set”\u003C/h3>\n\u003Cp>\u003Cstrong>Lösung:\u003C/strong> In \u003Ccode>config/config.php\u003C/code> Eintrag \u003Ccode>trusted_domains\u003C/code> korrekt konfigurieren:\u003C/p>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"php\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#9ECBFF\">'trusted_domains'\u003C/span>\u003Cspan style=\"color:#F97583\"> =>\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#79B8FF\">  array\u003C/span>\u003Cspan style=\"color:#E1E4E8\"> (\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#79B8FF\">    0\u003C/span>\u003Cspan style=\"color:#F97583\"> =>\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> 'yourdomain.tld'\u003C/span>\u003Cspan style=\"color:#E1E4E8\">,\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#79B8FF\">    1\u003C/span>\u003Cspan style=\"color:#F97583\"> =>\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> 'cloud.cc24.dev'\u003C/span>\u003Cspan style=\"color:#E1E4E8\">,\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#E1E4E8\">  ),\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Ch2 id=\"weiterführende-themen\">Weiterführende Themen\u003C/h2>\n\u003Cul>\n\u003Cli>\u003Cstrong>Integration mit Forensik-Plattformen\u003C/strong> (über WebDAV, API oder SSO)\u003C/li>\n\u003Cli>\u003Cstrong>Custom Apps entwickeln\u003C/strong> für spezielle Ermittlungs-Workflows\u003C/li>\n\u003Cli>\u003Cstrong>Auditing aktivieren\u003C/strong>: Nutzung und Änderungen nachvollziehen mit Protokollierungsfunktionen\u003C/li>\n\u003C/ul>",{"headings":557,"localImagePaths":587,"remoteImagePaths":588,"frontmatter":537,"imagePaths":589},[558,559,560,561,564,565,566,569,572,575,576,577,580,583,586],{"depth":43,"slug":44,"text":45},{"depth":47,"slug":48,"text":49},{"depth":51,"slug":52,"text":53},{"depth":51,"slug":562,"text":563},"installationsschritte-ubuntu-beispiel","Installationsschritte (Ubuntu Beispiel)",{"depth":47,"slug":58,"text":59},{"depth":47,"slug":70,"text":71},{"depth":51,"slug":567,"text":568},"gemeinsame-fallbearbeitung","Gemeinsame Fallbearbeitung",{"depth":51,"slug":570,"text":571},"videokonferenzen-mit-nextcloud-talk","Videokonferenzen mit “Nextcloud Talk”",{"depth":51,"slug":573,"text":574},"automatischer-dateiimport-per-api","Automatischer Dateiimport per API",{"depth":47,"slug":79,"text":80},{"depth":47,"slug":82,"text":83},{"depth":51,"slug":578,"text":579},"problem-langsame-performance","Problem: Langsame Performance",{"depth":51,"slug":581,"text":582},"problem-dateien-erscheinen-nicht-im-sync","Problem: Dateien erscheinen nicht im Sync",{"depth":51,"slug":584,"text":585},"problem-fehlermeldung-trusted-domain-not-set","Problem: Fehlermeldung “Trusted domain not set”",{"depth":47,"slug":94,"text":95},[],[],[],"nextcloud.md"]
\ No newline at end of file
+[["Map",1,2,7,8],"meta::meta",["Map",3,4,5,6],"astro-version","5.12.3","astro-config-digest","{\"root\":{},\"srcDir\":{},\"publicDir\":{},\"outDir\":{},\"cacheDir\":{},\"compressHTML\":true,\"base\":\"/\",\"trailingSlash\":\"ignore\",\"output\":\"server\",\"scopedStyleStrategy\":\"attribute\",\"build\":{\"format\":\"directory\",\"client\":{},\"server\":{},\"assets\":\"_astro\",\"serverEntry\":\"entry.mjs\",\"redirects\":true,\"inlineStylesheets\":\"auto\",\"concurrency\":1},\"server\":{\"open\":false,\"host\":true,\"port\":4321,\"streaming\":true,\"allowedHosts\":[]},\"redirects\":{},\"image\":{\"endpoint\":{\"route\":\"/_image\",\"entrypoint\":\"astro/assets/endpoint/node\"},\"service\":{\"entrypoint\":\"astro/assets/services/sharp\",\"config\":{}},\"domains\":[],\"remotePatterns\":[],\"responsiveStyles\":false},\"devToolbar\":{\"enabled\":true},\"markdown\":{\"syntaxHighlight\":{\"type\":\"shiki\",\"excludeLangs\":[\"math\"]},\"shikiConfig\":{\"langs\":[],\"langAlias\":{},\"theme\":\"github-dark\",\"themes\":{},\"wrap\":false,\"transformers\":[]},\"remarkPlugins\":[],\"rehypePlugins\":[],\"remarkRehype\":{},\"gfm\":true,\"smartypants\":true},\"security\":{\"checkOrigin\":true},\"env\":{\"schema\":{},\"validateSecrets\":false},\"experimental\":{\"clientPrerender\":false,\"contentIntellisense\":false,\"headingIdCompat\":false,\"preserveScriptOrder\":false,\"liveContentCollections\":false,\"csp\":false,\"rawEnvValues\":false},\"legacy\":{\"collections\":false},\"session\":{\"driver\":\"fs-lite\",\"options\":{\"base\":\"/var/home/user01/Projekte/forensic-pathways/node_modules/.astro/sessions\"}}}","knowledgebase",["Map",9,10,77,78,149,150,223,224,288,289,535,536],"regular-expressions-regex",{"id":9,"data":11,"body":33,"filePath":34,"digest":35,"rendered":36,"legacyId":76},{"title":12,"tool_name":13,"description":14,"last_updated":15,"author":16,"difficulty":17,"categories":18,"tags":23,"sections":29,"review_status":32},"Regular Expressions (Regex) – Musterbasierte Textanalyse","Regular Expressions (Regex)","Pattern matching language für Suche, Extraktion und Manipulation von Text in forensischen Analysen.",["Date","2025-07-20T00:00:00.000Z"],"Claude 4 Sonnet","intermediate",[19,20,21,22],"incident-response","malware-analysis","network-forensics","fraud-investigation",[24,25,26,27,28],"pattern-matching","text-processing","log-analysis","string-manipulation","search-algorithms",{"overview":30,"installation":31,"configuration":31,"usage_examples":30,"best_practices":30,"troubleshooting":31,"advanced_topics":30},true,false,"published","> **⚠️ Hinweis**: Dies ist ein vorläufiger, KI-generierter Knowledgebase-Eintrag. Wir freuen uns über Verbesserungen und Ergänzungen durch die Community!\n\n\n# Übersicht\n\n**Regular Expressions (Regex)** sind ein leistungsfähiges Werkzeug zur Erkennung, Extraktion und Transformation von Zeichenfolgen anhand vordefinierter Muster. In der digitalen Forensik sind Regex-Ausdrücke unverzichtbar: Sie helfen beim Auffinden von IP-Adressen, Hash-Werten, Dateipfaden, Malware-Signaturen oder Kreditkartennummern in großen Mengen unstrukturierter Daten wie Logdateien, Netzwerktraces oder Memory Dumps.\n\nRegex ist nicht auf eine bestimmte Plattform oder Software beschränkt – es wird in nahezu allen gängigen Programmiersprachen, Texteditoren und forensischen Tools unterstützt.\n\n## Verwendungsbeispiele\n\n### 1. IP-Adressen extrahieren\n\n```regex\n\\b(?:\\d{1,3}\\.){3}\\d{1,3}\\b\n````\n\nVerwendung:\n\n* Finden von IP-Adressen in Firewall-Logs oder Packet Captures.\n* Beispiel-Zeile:\n\n  ```\n  Connection from 192.168.1.101 to port 443 established\n  ```\n\n### 2. E-Mail-Adressen identifizieren\n\n```regex\n[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\\.[a-zA-Z]{2,}\n```\n\nVerwendung:\n\n* Erkennung von kompromittierten Accounts in Phishing-E-Mails.\n* Analyse von Useraktivitäten oder Kommunikationsverläufen.\n\n### 3. Hash-Werte erkennen (z. B. SHA-256)\n\n```regex\n\\b[A-Fa-f0-9]{64}\\b\n```\n\nVerwendung:\n\n* Extraktion von Malware-Hashes aus Memory Dumps oder YARA-Logs.\n\n### 4. Zeitstempel in Logdateien extrahieren\n\n```regex\n\\d{4}-\\d{2}-\\d{2}[ T]\\d{2}:\\d{2}:\\d{2}\n```\n\nVerwendung:\n\n* Zeitsensitive Korrelationsanalysen (z. B. bei Intrusion Detection oder Timeline-Rekonstruktionen).\n\n## Best Practices\n\n* **Regex testen**: Nutze Plattformen wie [regexr.com](https://regexr.com/) oder [regex101.com](https://regex101.com/) zur Validierung.\n* **Performance beachten**: Komplexe Ausdrücke können ineffizient sein und Systeme verlangsamen – verwende Lazy Quantifiers (`*?`, `+?`) bei Bedarf.\n* **Escape-Zeichen korrekt anwenden**: Spezielle Zeichen wie `.` oder `\\` müssen bei Bedarf mit `\\\\` oder `\\.` maskiert werden.\n* **Portabilität prüfen**: Unterschiedliche Regex-Engines (z. B. Python `re`, PCRE, JavaScript) interpretieren manche Syntax leicht unterschiedlich.\n* **Lesbarkeit fördern**: Verwende benannte Gruppen (`(?P\u003Cname>...)`) und Kommentare (`(?x)`), um reguläre Ausdrücke besser wartbar zu machen.\n\n## Weiterführende Themen\n\n### Lookaheads und Lookbehinds\n\nMit **Lookaheads** (`(?=...)`) und **Lookbehinds** (`(?\u003C=...)`) können Bedingungen formuliert werden, ohne dass der Text Teil des Matchs wird.\n\nBeispiel: Alle `.exe`-Dateinamen **ohne** das Wort `safe` davor matchen:\n\n```regex\n(?\u003C!safe\\s)[\\w-]+\\.exe\n```\n\n### Regex in Forensik-Tools\n\n* **YARA**: Unterstützt Regex zur Erstellung von Malware-Signaturen.\n* **Wireshark**: Filtert Payloads anhand von Regex-ähnlicher Syntax.\n* **Splunk & ELK**: Verwenden Regex für Logparsing und Visualisierung.\n* **Volatility Plugins**: Extrahieren Artefakte mit Regex-basierten Scans.\n\n---\n\n> 🔤 **Regex ist ein universelles Werkzeug für Analysten, Ermittler und Entwickler, um versteckte Informationen schnell und flexibel aufzuspüren.**\n>\n> Nutze es überall dort, wo Textdaten eine Rolle spielen.","src/content/knowledgebase/regular-expressions-regex.md","247bcf48ebdc9ba0",{"html":37,"metadata":38},"\u003Cblockquote>\n\u003Cp>\u003Cstrong>⚠️ Hinweis\u003C/strong>: Dies ist ein vorläufiger, KI-generierter Knowledgebase-Eintrag. Wir freuen uns über Verbesserungen und Ergänzungen durch die Community!\u003C/p>\n\u003C/blockquote>\n\u003Ch1 id=\"übersicht\">Übersicht\u003C/h1>\n\u003Cp>\u003Cstrong>Regular Expressions (Regex)\u003C/strong> sind ein leistungsfähiges Werkzeug zur Erkennung, Extraktion und Transformation von Zeichenfolgen anhand vordefinierter Muster. In der digitalen Forensik sind Regex-Ausdrücke unverzichtbar: Sie helfen beim Auffinden von IP-Adressen, Hash-Werten, Dateipfaden, Malware-Signaturen oder Kreditkartennummern in großen Mengen unstrukturierter Daten wie Logdateien, Netzwerktraces oder Memory Dumps.\u003C/p>\n\u003Cp>Regex ist nicht auf eine bestimmte Plattform oder Software beschränkt – es wird in nahezu allen gängigen Programmiersprachen, Texteditoren und forensischen Tools unterstützt.\u003C/p>\n\u003Ch2 id=\"verwendungsbeispiele\">Verwendungsbeispiele\u003C/h2>\n\u003Ch3 id=\"1-ip-adressen-extrahieren\">1. IP-Adressen extrahieren\u003C/h3>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"regex\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#79B8FF\">\\b(?:\\d\u003C/span>\u003Cspan style=\"color:#F97583\">{1,3}\u003C/span>\u003Cspan style=\"color:#79B8FF\">\\.)\u003C/span>\u003Cspan style=\"color:#F97583\">{3}\u003C/span>\u003Cspan style=\"color:#79B8FF\">\\d\u003C/span>\u003Cspan style=\"color:#F97583\">{1,3}\u003C/span>\u003Cspan style=\"color:#79B8FF\">\\b\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Cp>Verwendung:\u003C/p>\n\u003Cul>\n\u003Cli>\n\u003Cp>Finden von IP-Adressen in Firewall-Logs oder Packet Captures.\u003C/p>\n\u003C/li>\n\u003Cli>\n\u003Cp>Beispiel-Zeile:\u003C/p>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"plaintext\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan>Connection from 192.168.1.101 to port 443 established\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003C/li>\n\u003C/ul>\n\u003Ch3 id=\"2-e-mail-adressen-identifizieren\">2. E-Mail-Adressen identifizieren\u003C/h3>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"regex\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#79B8FF\">[a-zA-Z0-9._%+-]\u003C/span>\u003Cspan style=\"color:#F97583\">+\u003C/span>\u003Cspan style=\"color:#DBEDFF\">@\u003C/span>\u003Cspan style=\"color:#79B8FF\">[a-zA-Z0-9.-]\u003C/span>\u003Cspan style=\"color:#F97583\">+\u003C/span>\u003Cspan style=\"color:#79B8FF\">\\.[a-zA-Z]\u003C/span>\u003Cspan style=\"color:#F97583\">{2,}\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Cp>Verwendung:\u003C/p>\n\u003Cul>\n\u003Cli>Erkennung von kompromittierten Accounts in Phishing-E-Mails.\u003C/li>\n\u003Cli>Analyse von Useraktivitäten oder Kommunikationsverläufen.\u003C/li>\n\u003C/ul>\n\u003Ch3 id=\"3-hash-werte-erkennen-zb-sha-256\">3. Hash-Werte erkennen (z. B. SHA-256)\u003C/h3>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"regex\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#79B8FF\">\\b[A-Fa-f0-9]\u003C/span>\u003Cspan style=\"color:#F97583\">{64}\u003C/span>\u003Cspan style=\"color:#79B8FF\">\\b\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Cp>Verwendung:\u003C/p>\n\u003Cul>\n\u003Cli>Extraktion von Malware-Hashes aus Memory Dumps oder YARA-Logs.\u003C/li>\n\u003C/ul>\n\u003Ch3 id=\"4-zeitstempel-in-logdateien-extrahieren\">4. Zeitstempel in Logdateien extrahieren\u003C/h3>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"regex\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#79B8FF\">\\d\u003C/span>\u003Cspan style=\"color:#F97583\">{4}\u003C/span>\u003Cspan style=\"color:#DBEDFF\">-\u003C/span>\u003Cspan style=\"color:#79B8FF\">\\d\u003C/span>\u003Cspan style=\"color:#F97583\">{2}\u003C/span>\u003Cspan style=\"color:#DBEDFF\">-\u003C/span>\u003Cspan style=\"color:#79B8FF\">\\d\u003C/span>\u003Cspan style=\"color:#F97583\">{2}\u003C/span>\u003Cspan style=\"color:#79B8FF\">[ T]\\d\u003C/span>\u003Cspan style=\"color:#F97583\">{2}\u003C/span>\u003Cspan style=\"color:#DBEDFF\">:\u003C/span>\u003Cspan style=\"color:#79B8FF\">\\d\u003C/span>\u003Cspan style=\"color:#F97583\">{2}\u003C/span>\u003Cspan style=\"color:#DBEDFF\">:\u003C/span>\u003Cspan style=\"color:#79B8FF\">\\d\u003C/span>\u003Cspan style=\"color:#F97583\">{2}\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Cp>Verwendung:\u003C/p>\n\u003Cul>\n\u003Cli>Zeitsensitive Korrelationsanalysen (z. B. bei Intrusion Detection oder Timeline-Rekonstruktionen).\u003C/li>\n\u003C/ul>\n\u003Ch2 id=\"best-practices\">Best Practices\u003C/h2>\n\u003Cul>\n\u003Cli>\u003Cstrong>Regex testen\u003C/strong>: Nutze Plattformen wie \u003Ca href=\"https://regexr.com/\">regexr.com\u003C/a> oder \u003Ca href=\"https://regex101.com/\">regex101.com\u003C/a> zur Validierung.\u003C/li>\n\u003Cli>\u003Cstrong>Performance beachten\u003C/strong>: Komplexe Ausdrücke können ineffizient sein und Systeme verlangsamen – verwende Lazy Quantifiers (\u003Ccode>*?\u003C/code>, \u003Ccode>+?\u003C/code>) bei Bedarf.\u003C/li>\n\u003Cli>\u003Cstrong>Escape-Zeichen korrekt anwenden\u003C/strong>: Spezielle Zeichen wie \u003Ccode>.\u003C/code> oder \u003Ccode>\\\u003C/code> müssen bei Bedarf mit \u003Ccode>\\\\\u003C/code> oder \u003Ccode>\\.\u003C/code> maskiert werden.\u003C/li>\n\u003Cli>\u003Cstrong>Portabilität prüfen\u003C/strong>: Unterschiedliche Regex-Engines (z. B. Python \u003Ccode>re\u003C/code>, PCRE, JavaScript) interpretieren manche Syntax leicht unterschiedlich.\u003C/li>\n\u003Cli>\u003Cstrong>Lesbarkeit fördern\u003C/strong>: Verwende benannte Gruppen (\u003Ccode>(?P<name>...)\u003C/code>) und Kommentare (\u003Ccode>(?x)\u003C/code>), um reguläre Ausdrücke besser wartbar zu machen.\u003C/li>\n\u003C/ul>\n\u003Ch2 id=\"weiterführende-themen\">Weiterführende Themen\u003C/h2>\n\u003Ch3 id=\"lookaheads-und-lookbehinds\">Lookaheads und Lookbehinds\u003C/h3>\n\u003Cp>Mit \u003Cstrong>Lookaheads\u003C/strong> (\u003Ccode>(?=...)\u003C/code>) und \u003Cstrong>Lookbehinds\u003C/strong> (\u003Ccode>(?<=...)\u003C/code>) können Bedingungen formuliert werden, ohne dass der Text Teil des Matchs wird.\u003C/p>\n\u003Cp>Beispiel: Alle \u003Ccode>.exe\u003C/code>-Dateinamen \u003Cstrong>ohne\u003C/strong> das Wort \u003Ccode>safe\u003C/code> davor matchen:\u003C/p>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"regex\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#F97583\">(?<!\u003C/span>\u003Cspan style=\"color:#DBEDFF\">safe\u003C/span>\u003Cspan style=\"color:#79B8FF\">\\s\u003C/span>\u003Cspan style=\"color:#F97583\">)\u003C/span>\u003Cspan style=\"color:#79B8FF\">[\\w-]\u003C/span>\u003Cspan style=\"color:#F97583\">+\u003C/span>\u003Cspan style=\"color:#79B8FF\">\\.\u003C/span>\u003Cspan style=\"color:#DBEDFF\">exe\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Ch3 id=\"regex-in-forensik-tools\">Regex in Forensik-Tools\u003C/h3>\n\u003Cul>\n\u003Cli>\u003Cstrong>YARA\u003C/strong>: Unterstützt Regex zur Erstellung von Malware-Signaturen.\u003C/li>\n\u003Cli>\u003Cstrong>Wireshark\u003C/strong>: Filtert Payloads anhand von Regex-ähnlicher Syntax.\u003C/li>\n\u003Cli>\u003Cstrong>Splunk & ELK\u003C/strong>: Verwenden Regex für Logparsing und Visualisierung.\u003C/li>\n\u003Cli>\u003Cstrong>Volatility Plugins\u003C/strong>: Extrahieren Artefakte mit Regex-basierten Scans.\u003C/li>\n\u003C/ul>\n\u003Chr>\n\u003Cblockquote>\n\u003Cp>🔤 \u003Cstrong>Regex ist ein universelles Werkzeug für Analysten, Ermittler und Entwickler, um versteckte Informationen schnell und flexibel aufzuspüren.\u003C/strong>\u003C/p>\n\u003Cp>Nutze es überall dort, wo Textdaten eine Rolle spielen.\u003C/p>\n\u003C/blockquote>",{"headings":39,"localImagePaths":73,"remoteImagePaths":74,"frontmatter":11,"imagePaths":75},[40,44,48,52,55,58,61,64,67,70],{"depth":41,"slug":42,"text":43},1,"übersicht","Übersicht",{"depth":45,"slug":46,"text":47},2,"verwendungsbeispiele","Verwendungsbeispiele",{"depth":49,"slug":50,"text":51},3,"1-ip-adressen-extrahieren","1. IP-Adressen extrahieren",{"depth":49,"slug":53,"text":54},"2-e-mail-adressen-identifizieren","2. E-Mail-Adressen identifizieren",{"depth":49,"slug":56,"text":57},"3-hash-werte-erkennen-zb-sha-256","3. Hash-Werte erkennen (z. B. SHA-256)",{"depth":49,"slug":59,"text":60},"4-zeitstempel-in-logdateien-extrahieren","4. Zeitstempel in Logdateien extrahieren",{"depth":45,"slug":62,"text":63},"best-practices","Best Practices",{"depth":45,"slug":65,"text":66},"weiterführende-themen","Weiterführende Themen",{"depth":49,"slug":68,"text":69},"lookaheads-und-lookbehinds","Lookaheads und Lookbehinds",{"depth":49,"slug":71,"text":72},"regex-in-forensik-tools","Regex in Forensik-Tools",[],[],[],"regular-expressions-regex.md","misp",{"id":77,"data":79,"body":95,"filePath":96,"digest":97,"rendered":98,"legacyId":148},{"title":80,"tool_name":81,"description":82,"last_updated":83,"author":16,"difficulty":17,"categories":84,"tags":87,"sections":94,"review_status":32},"MISP - Plattform für Threat Intelligence Sharing","MISP","Das Rückgrat des modernen Threat-Intelligence-Sharings mit über 40.000 aktiven Instanzen weltweit.",["Date","2025-07-20T00:00:00.000Z"],[19,85,20,21,86],"static-investigations","cloud-forensics",[88,89,90,91,92,93],"web-based","threat-intelligence","api","correlation","ioc-sharing","automation",{"overview":30,"installation":30,"configuration":30,"usage_examples":30,"best_practices":30,"troubleshooting":30,"advanced_topics":31},"> **⚠️ Hinweis**: Dies ist ein vorläufiger, KI-generierter Knowledgebase-Eintrag. Wir freuen uns über Verbesserungen und Ergänzungen durch die Community!\n\n\n# Übersicht\n\n**MISP (Malware Information Sharing Platform & Threat Sharing)** ist eine freie Open-Source-Plattform zur strukturierten Erfassung, Speicherung, Analyse und gemeinsamen Nutzung von Cyber-Bedrohungsdaten. Mit über 40.000 Instanzen weltweit ist MISP der De-facto-Standard für den Austausch von Indicators of Compromise (IoCs) und Threat Intelligence zwischen CERTs, SOCs, Strafverfolgungsbehörden und anderen sicherheitsrelevanten Organisationen.\n\nDie föderierte Architektur ermöglicht einen kontrollierten, dezentralen Austausch von Informationen über vertrauenswürdige Partner hinweg. Durch Taxonomien, Tags und integrierte APIs ist eine automatische Anreicherung, Korrelation und Verarbeitung von Informationen in SIEMs, Firewalls oder Endpoint-Lösungen möglich.\n\n## Installation\n\n### Voraussetzungen\n\n- **Server-Betriebssystem:** Linux (empfohlen: Debian/Ubuntu)\n- **Abhängigkeiten:** MariaDB/MySQL, PHP, Apache/Nginx, Redis\n- **Ressourcen:** Mindestens 4 GB RAM, SSD empfohlen\n\n### Installationsschritte\n\n```bash\n# Beispiel für Debian/Ubuntu:\nsudo apt update && sudo apt install -y curl gnupg git python3 python3-pip redis-server mariadb-server apache2 php libapache2-mod-php\n\n# MISP klonen\ngit clone https://github.com/MISP/MISP.git /var/www/MISP\n\n# Setup-Skript nutzen\ncd /var/www/MISP && bash INSTALL/INSTALL.debian.sh\n````\n\nWeitere Details: [Offizielle Installationsanleitung](https://misp.github.io/MISP/INSTALL.debian/)\n\n## Konfiguration\n\n### Webserver\n\n* HTTPS aktivieren (Let's Encrypt oder Reverse Proxy)\n* PHP-Konfiguration anpassen (`upload_max_filesize`, `memory_limit`, `post_max_size`)\n\n### Benutzerrollen\n\n* Administrator, Org-Admin, Analyst etc.\n* Zugriffsbeschränkungen nach Organisation/Feed definierbar\n\n### Feeds und Galaxies\n\n* Aktivierung von Feeds (z. B. CIRCL, Abuse.ch, OpenCTI)\n* Nutzung von Galaxies zur Klassifizierung (APT-Gruppen, Malware-Familien)\n\n## Verwendungsbeispiele\n\n### Beispiel 1: Import von IoCs aus externem Feed\n\n1. Feed aktivieren unter **Administration → List Feeds**\n2. Feed synchronisieren\n3. Ereignisse durchsuchen, analysieren, ggf. mit eigenen Daten korrelieren\n\n### Beispiel 2: Automatisierte Anbindung an SIEM\n\n* REST-API-Token erstellen\n* API-Calls zur Abfrage neuer Events (z. B. mit Python, Logstash oder MISP Workbench)\n* Integration in Security-Systeme über JSON/STIX export\n\n## Best Practices\n\n* Regelmäßige Backups der Datenbank\n* Taxonomien konsistent verwenden\n* Nutzung der Sighting-Funktion zur Validierung von IoCs\n* Vertrauensstufen (TLP, PAP) korrekt setzen\n* Nicht nur konsumieren – auch teilen!\n\n## Troubleshooting\n\n### Problem: MISP-Feeds laden nicht\n\n**Lösung:**\n\n* Internetverbindung prüfen\n* Cronjobs aktiv?\n* Logs prüfen: `/var/www/MISP/app/tmp/logs/error.log`\n\n### Problem: API gibt 403 zurück\n\n**Lösung:**\n\n* Ist der API-Key korrekt und aktiv?\n* Rechte des Benutzers überprüfen\n* IP-Filter im MISP-Backend beachten\n\n### Problem: Hohe Datenbanklast\n\n**Lösung:**\n\n* Indizes optimieren\n* Redis aktivieren\n* Alte Events regelmäßig archivieren oder löschen\n\n## Weiterführende Themen\n\n* STIX2-Import/Export\n* Erweiterungen mit MISP Modules (z. B. für Virustotal, YARA)\n* Föderierte Netzwerke und Community-Portale\n* Integration mit OpenCTI oder TheHive\n\n---\n\n**Links:**\n\n* 🌐 [Offizielle Projektseite](https://misp-project.org/)\n* 📦 [CC24-MISP-Instanz](https://misp.cc24.dev)\n* 📊 [Status-Monitoring](https://status.mikoshi.de/api/badge/34/status)\n\nLizenz: **AGPL-3.0**","src/content/knowledgebase/misp.md","35930fa919a46964",{"html":99,"metadata":100},"\u003Cblockquote>\n\u003Cp>\u003Cstrong>⚠️ Hinweis\u003C/strong>: Dies ist ein vorläufiger, KI-generierter Knowledgebase-Eintrag. Wir freuen uns über Verbesserungen und Ergänzungen durch die Community!\u003C/p>\n\u003C/blockquote>\n\u003Ch1 id=\"übersicht\">Übersicht\u003C/h1>\n\u003Cp>\u003Cstrong>MISP (Malware Information Sharing Platform & Threat Sharing)\u003C/strong> ist eine freie Open-Source-Plattform zur strukturierten Erfassung, Speicherung, Analyse und gemeinsamen Nutzung von Cyber-Bedrohungsdaten. Mit über 40.000 Instanzen weltweit ist MISP der De-facto-Standard für den Austausch von Indicators of Compromise (IoCs) und Threat Intelligence zwischen CERTs, SOCs, Strafverfolgungsbehörden und anderen sicherheitsrelevanten Organisationen.\u003C/p>\n\u003Cp>Die föderierte Architektur ermöglicht einen kontrollierten, dezentralen Austausch von Informationen über vertrauenswürdige Partner hinweg. Durch Taxonomien, Tags und integrierte APIs ist eine automatische Anreicherung, Korrelation und Verarbeitung von Informationen in SIEMs, Firewalls oder Endpoint-Lösungen möglich.\u003C/p>\n\u003Ch2 id=\"installation\">Installation\u003C/h2>\n\u003Ch3 id=\"voraussetzungen\">Voraussetzungen\u003C/h3>\n\u003Cul>\n\u003Cli>\u003Cstrong>Server-Betriebssystem:\u003C/strong> Linux (empfohlen: Debian/Ubuntu)\u003C/li>\n\u003Cli>\u003Cstrong>Abhängigkeiten:\u003C/strong> MariaDB/MySQL, PHP, Apache/Nginx, Redis\u003C/li>\n\u003Cli>\u003Cstrong>Ressourcen:\u003C/strong> Mindestens 4 GB RAM, SSD empfohlen\u003C/li>\n\u003C/ul>\n\u003Ch3 id=\"installationsschritte\">Installationsschritte\u003C/h3>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"bash\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># Beispiel für Debian/Ubuntu:\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">sudo\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> apt\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> update\u003C/span>\u003Cspan style=\"color:#E1E4E8\"> && \u003C/span>\u003Cspan style=\"color:#B392F0\">sudo\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> apt\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> install\u003C/span>\u003Cspan style=\"color:#79B8FF\"> -y\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> curl\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> gnupg\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> git\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> python3\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> python3-pip\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> redis-server\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> mariadb-server\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> apache2\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> php\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> libapache2-mod-php\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># MISP klonen\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">git\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> clone\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> https://github.com/MISP/MISP.git\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> /var/www/MISP\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># Setup-Skript nutzen\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#79B8FF\">cd\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> /var/www/MISP\u003C/span>\u003Cspan style=\"color:#E1E4E8\"> && \u003C/span>\u003Cspan style=\"color:#B392F0\">bash\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> INSTALL/INSTALL.debian.sh\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Cp>Weitere Details: \u003Ca href=\"https://misp.github.io/MISP/INSTALL.debian/\">Offizielle Installationsanleitung\u003C/a>\u003C/p>\n\u003Ch2 id=\"konfiguration\">Konfiguration\u003C/h2>\n\u003Ch3 id=\"webserver\">Webserver\u003C/h3>\n\u003Cul>\n\u003Cli>HTTPS aktivieren (Let’s Encrypt oder Reverse Proxy)\u003C/li>\n\u003Cli>PHP-Konfiguration anpassen (\u003Ccode>upload_max_filesize\u003C/code>, \u003Ccode>memory_limit\u003C/code>, \u003Ccode>post_max_size\u003C/code>)\u003C/li>\n\u003C/ul>\n\u003Ch3 id=\"benutzerrollen\">Benutzerrollen\u003C/h3>\n\u003Cul>\n\u003Cli>Administrator, Org-Admin, Analyst etc.\u003C/li>\n\u003Cli>Zugriffsbeschränkungen nach Organisation/Feed definierbar\u003C/li>\n\u003C/ul>\n\u003Ch3 id=\"feeds-und-galaxies\">Feeds und Galaxies\u003C/h3>\n\u003Cul>\n\u003Cli>Aktivierung von Feeds (z. B. CIRCL, Abuse.ch, OpenCTI)\u003C/li>\n\u003Cli>Nutzung von Galaxies zur Klassifizierung (APT-Gruppen, Malware-Familien)\u003C/li>\n\u003C/ul>\n\u003Ch2 id=\"verwendungsbeispiele\">Verwendungsbeispiele\u003C/h2>\n\u003Ch3 id=\"beispiel-1-import-von-iocs-aus-externem-feed\">Beispiel 1: Import von IoCs aus externem Feed\u003C/h3>\n\u003Col>\n\u003Cli>Feed aktivieren unter \u003Cstrong>Administration → List Feeds\u003C/strong>\u003C/li>\n\u003Cli>Feed synchronisieren\u003C/li>\n\u003Cli>Ereignisse durchsuchen, analysieren, ggf. mit eigenen Daten korrelieren\u003C/li>\n\u003C/ol>\n\u003Ch3 id=\"beispiel-2-automatisierte-anbindung-an-siem\">Beispiel 2: Automatisierte Anbindung an SIEM\u003C/h3>\n\u003Cul>\n\u003Cli>REST-API-Token erstellen\u003C/li>\n\u003Cli>API-Calls zur Abfrage neuer Events (z. B. mit Python, Logstash oder MISP Workbench)\u003C/li>\n\u003Cli>Integration in Security-Systeme über JSON/STIX export\u003C/li>\n\u003C/ul>\n\u003Ch2 id=\"best-practices\">Best Practices\u003C/h2>\n\u003Cul>\n\u003Cli>Regelmäßige Backups der Datenbank\u003C/li>\n\u003Cli>Taxonomien konsistent verwenden\u003C/li>\n\u003Cli>Nutzung der Sighting-Funktion zur Validierung von IoCs\u003C/li>\n\u003Cli>Vertrauensstufen (TLP, PAP) korrekt setzen\u003C/li>\n\u003Cli>Nicht nur konsumieren – auch teilen!\u003C/li>\n\u003C/ul>\n\u003Ch2 id=\"troubleshooting\">Troubleshooting\u003C/h2>\n\u003Ch3 id=\"problem-misp-feeds-laden-nicht\">Problem: MISP-Feeds laden nicht\u003C/h3>\n\u003Cp>\u003Cstrong>Lösung:\u003C/strong>\u003C/p>\n\u003Cul>\n\u003Cli>Internetverbindung prüfen\u003C/li>\n\u003Cli>Cronjobs aktiv?\u003C/li>\n\u003Cli>Logs prüfen: \u003Ccode>/var/www/MISP/app/tmp/logs/error.log\u003C/code>\u003C/li>\n\u003C/ul>\n\u003Ch3 id=\"problem-api-gibt-403-zurück\">Problem: API gibt 403 zurück\u003C/h3>\n\u003Cp>\u003Cstrong>Lösung:\u003C/strong>\u003C/p>\n\u003Cul>\n\u003Cli>Ist der API-Key korrekt und aktiv?\u003C/li>\n\u003Cli>Rechte des Benutzers überprüfen\u003C/li>\n\u003Cli>IP-Filter im MISP-Backend beachten\u003C/li>\n\u003C/ul>\n\u003Ch3 id=\"problem-hohe-datenbanklast\">Problem: Hohe Datenbanklast\u003C/h3>\n\u003Cp>\u003Cstrong>Lösung:\u003C/strong>\u003C/p>\n\u003Cul>\n\u003Cli>Indizes optimieren\u003C/li>\n\u003Cli>Redis aktivieren\u003C/li>\n\u003Cli>Alte Events regelmäßig archivieren oder löschen\u003C/li>\n\u003C/ul>\n\u003Ch2 id=\"weiterführende-themen\">Weiterführende Themen\u003C/h2>\n\u003Cul>\n\u003Cli>STIX2-Import/Export\u003C/li>\n\u003Cli>Erweiterungen mit MISP Modules (z. B. für Virustotal, YARA)\u003C/li>\n\u003Cli>Föderierte Netzwerke und Community-Portale\u003C/li>\n\u003Cli>Integration mit OpenCTI oder TheHive\u003C/li>\n\u003C/ul>\n\u003Chr>\n\u003Cp>\u003Cstrong>Links:\u003C/strong>\u003C/p>\n\u003Cul>\n\u003Cli>🌐 \u003Ca href=\"https://misp-project.org/\">Offizielle Projektseite\u003C/a>\u003C/li>\n\u003Cli>📦 \u003Ca href=\"https://misp.cc24.dev\">CC24-MISP-Instanz\u003C/a>\u003C/li>\n\u003Cli>📊 \u003Ca href=\"https://status.mikoshi.de/api/badge/34/status\">Status-Monitoring\u003C/a>\u003C/li>\n\u003C/ul>\n\u003Cp>Lizenz: \u003Cstrong>AGPL-3.0\u003C/strong>\u003C/p>",{"headings":101,"localImagePaths":145,"remoteImagePaths":146,"frontmatter":79,"imagePaths":147},[102,103,106,109,112,115,118,121,124,125,128,131,132,135,138,141,144],{"depth":41,"slug":42,"text":43},{"depth":45,"slug":104,"text":105},"installation","Installation",{"depth":49,"slug":107,"text":108},"voraussetzungen","Voraussetzungen",{"depth":49,"slug":110,"text":111},"installationsschritte","Installationsschritte",{"depth":45,"slug":113,"text":114},"konfiguration","Konfiguration",{"depth":49,"slug":116,"text":117},"webserver","Webserver",{"depth":49,"slug":119,"text":120},"benutzerrollen","Benutzerrollen",{"depth":49,"slug":122,"text":123},"feeds-und-galaxies","Feeds und Galaxies",{"depth":45,"slug":46,"text":47},{"depth":49,"slug":126,"text":127},"beispiel-1-import-von-iocs-aus-externem-feed","Beispiel 1: Import von IoCs aus externem Feed",{"depth":49,"slug":129,"text":130},"beispiel-2-automatisierte-anbindung-an-siem","Beispiel 2: Automatisierte Anbindung an SIEM",{"depth":45,"slug":62,"text":63},{"depth":45,"slug":133,"text":134},"troubleshooting","Troubleshooting",{"depth":49,"slug":136,"text":137},"problem-misp-feeds-laden-nicht","Problem: MISP-Feeds laden nicht",{"depth":49,"slug":139,"text":140},"problem-api-gibt-403-zurück","Problem: API gibt 403 zurück",{"depth":49,"slug":142,"text":143},"problem-hohe-datenbanklast","Problem: Hohe Datenbanklast",{"depth":45,"slug":65,"text":66},[],[],[],"misp.md","kali-linux",{"id":149,"data":151,"body":166,"filePath":167,"digest":168,"rendered":169,"legacyId":222},{"title":152,"tool_name":153,"description":154,"last_updated":155,"author":16,"difficulty":17,"categories":156,"tags":159,"sections":165,"review_status":32},"Kali Linux - Die Hacker-Distribution für Forensik & Penetration Testing","Kali Linux","Leitfaden zur Installation, Nutzung und Best Practices für Kali Linux – die All-in-One-Plattform für Security-Profis.",["Date","2025-07-20T00:00:00.000Z"],[19,157,158],"forensics","penetration-testing",[160,161,158,162,163,164],"live-boot","tool-collection","forensics-suite","virtualization","arm-support",{"overview":30,"installation":30,"configuration":30,"usage_examples":30,"best_practices":30,"troubleshooting":30,"advanced_topics":30},"> **⚠️ Hinweis**: Dies ist ein vorläufiger, KI-generierter Knowledgebase-Eintrag. Wir freuen uns über Verbesserungen und Ergänzungen durch die Community!\n\n\n# Übersicht\n\nKali Linux ist eine auf Debian basierende Linux-Distribution, die speziell für Penetration Testing, digitale Forensik, Reverse Engineering und Incident Response entwickelt wurde. Mit über 600 vorinstallierten Tools ist sie ein unverzichtbares Werkzeug für Security-Experten, Ermittler und forensische Analysten. Die Live-Boot-Funktion erlaubt es, Systeme ohne Spuren zu hinterlassen zu analysieren – ideal für forensische Untersuchungen.\n\n## Installation\n\n### Option 1: Live-System (USB/DVD)\n\n1. ISO-Image von [kali.org](https://www.kali.org/get-kali/) herunterladen.\n2. Mit **Rufus** oder **balenaEtcher** auf einen USB-Stick schreiben.\n3. Vom USB-Stick booten (ggf. Boot-Reihenfolge im BIOS anpassen).\n4. Kali kann direkt ohne Installation im Live-Modus verwendet werden.\n\n### Option 2: Installation auf Festplatte\n\n1. ISO-Image booten und **Graphical Install** wählen.\n2. Schritt-für-Schritt durch den Installationsassistenten navigieren:\n   - Sprache, Zeitzone und Tastaturlayout auswählen\n   - Partitionierung konfigurieren (automatisch oder manuell)\n   - Benutzerkonten erstellen\n3. Nach Installation Neustart durchführen.\n\n### Option 3: Virtuelle Maschine (VM)\n\n- Offizielle VM-Images für VirtualBox und VMware von der [Kali-Website](https://www.kali.org/get-kali/#kali-virtual-machines)\n- Importieren, ggf. Netzwerkbrücke und Shared Folders aktivieren\n\n## Konfiguration\n\n### Netzwerkeinstellungen\n\n- Konfiguration über `nmtui` oder `/etc/network/interfaces`\n- VPN und Proxy-Integration über GUI oder Terminal\n\n### Updates & Paketquellen\n\n```bash\nsudo apt update && sudo apt full-upgrade\n````\n\n> Hinweis: `kali-rolling` ist die Standard-Distribution für kontinuierliche Updates.\n\n### Sprache & Lokalisierung\n\n```bash\nsudo dpkg-reconfigure locales\nsudo dpkg-reconfigure keyboard-configuration\n```\n\n## Verwendungsbeispiele\n\n### 1. Netzwerkscan mit Nmap\n\n```bash\nnmap -sS -T4 -A 192.168.1.0/24\n```\n\n### 2. Passwort-Cracking mit John the Ripper\n\n```bash\njohn --wordlist=/usr/share/wordlists/rockyou.txt hashes.txt\n```\n\n### 3. Forensik mit Autopsy\n\n```bash\nautopsy &\n```\n\n### 4. Android-Analyse mit MobSF (in Docker)\n\n```bash\ndocker pull opensecurity/mobile-security-framework-mobsf\ndocker run -it -p 8000:8000 mobsf\n```\n\n## Best Practices\n\n* Nutze immer **aktuelle Snapshots** oder VM-Clones vor gefährlichen Tests\n* Verwende separate Netzwerke (z. B. Host-only oder NAT) für Tests\n* Deaktiviere automatisches WLAN bei forensischen Analysen\n* Prüfe und aktualisiere regelmäßig Toolsets (`apt`, `git`, `pip`)\n* Halte deine ISO-Images versioniert für forensische Reproduzierbarkeit\n\n## Troubleshooting\n\n### Problem: Keine Internetverbindung nach Installation\n\n**Lösung:** Netzwerkadapter prüfen, ggf. mit `ifconfig` oder `ip a` überprüfen, DHCP aktivieren.\n\n### Problem: Tools fehlen nach Update\n\n**Lösung:** Tool-Gruppen wie `kali-linux-default` manuell nachinstallieren:\n\n```bash\nsudo apt install kali-linux-default\n```\n\n### Problem: „Permission Denied“ bei Tools\n\n**Lösung:** Root-Rechte nutzen oder mit `sudo` ausführen.\n\n## Weiterführende Themen\n\n* **Kustomisierung von Kali ISOs** mit `live-build`\n* **NetHunter**: Kali für mobile Geräte (Android)\n* **Kali Purple**: Defensive Security Suite\n* Integration mit **Cloud-Infrastrukturen** via WSL oder Azure\n\n---\n\n**Links & Ressourcen:**\n\n* Offizielle Website: [https://kali.org](https://kali.org/)\n* Dokumentation: [https://docs.kali.org/](https://docs.kali.org/)\n* GitLab Repo: [https://gitlab.com/kalilinux](https://gitlab.com/kalilinux)\n* Discord-Community: [https://discord.com/invite/kali-linux](https://discord.com/invite/kali-linux)","src/content/knowledgebase/kali-linux.md","09243ebc79d75dbc",{"html":170,"metadata":171},"\u003Cblockquote>\n\u003Cp>\u003Cstrong>⚠️ Hinweis\u003C/strong>: Dies ist ein vorläufiger, KI-generierter Knowledgebase-Eintrag. Wir freuen uns über Verbesserungen und Ergänzungen durch die Community!\u003C/p>\n\u003C/blockquote>\n\u003Ch1 id=\"übersicht\">Übersicht\u003C/h1>\n\u003Cp>Kali Linux ist eine auf Debian basierende Linux-Distribution, die speziell für Penetration Testing, digitale Forensik, Reverse Engineering und Incident Response entwickelt wurde. Mit über 600 vorinstallierten Tools ist sie ein unverzichtbares Werkzeug für Security-Experten, Ermittler und forensische Analysten. Die Live-Boot-Funktion erlaubt es, Systeme ohne Spuren zu hinterlassen zu analysieren – ideal für forensische Untersuchungen.\u003C/p>\n\u003Ch2 id=\"installation\">Installation\u003C/h2>\n\u003Ch3 id=\"option-1-live-system-usbdvd\">Option 1: Live-System (USB/DVD)\u003C/h3>\n\u003Col>\n\u003Cli>ISO-Image von \u003Ca href=\"https://www.kali.org/get-kali/\">kali.org\u003C/a> herunterladen.\u003C/li>\n\u003Cli>Mit \u003Cstrong>Rufus\u003C/strong> oder \u003Cstrong>balenaEtcher\u003C/strong> auf einen USB-Stick schreiben.\u003C/li>\n\u003Cli>Vom USB-Stick booten (ggf. Boot-Reihenfolge im BIOS anpassen).\u003C/li>\n\u003Cli>Kali kann direkt ohne Installation im Live-Modus verwendet werden.\u003C/li>\n\u003C/ol>\n\u003Ch3 id=\"option-2-installation-auf-festplatte\">Option 2: Installation auf Festplatte\u003C/h3>\n\u003Col>\n\u003Cli>ISO-Image booten und \u003Cstrong>Graphical Install\u003C/strong> wählen.\u003C/li>\n\u003Cli>Schritt-für-Schritt durch den Installationsassistenten navigieren:\n\u003Cul>\n\u003Cli>Sprache, Zeitzone und Tastaturlayout auswählen\u003C/li>\n\u003Cli>Partitionierung konfigurieren (automatisch oder manuell)\u003C/li>\n\u003Cli>Benutzerkonten erstellen\u003C/li>\n\u003C/ul>\n\u003C/li>\n\u003Cli>Nach Installation Neustart durchführen.\u003C/li>\n\u003C/ol>\n\u003Ch3 id=\"option-3-virtuelle-maschine-vm\">Option 3: Virtuelle Maschine (VM)\u003C/h3>\n\u003Cul>\n\u003Cli>Offizielle VM-Images für VirtualBox und VMware von der \u003Ca href=\"https://www.kali.org/get-kali/#kali-virtual-machines\">Kali-Website\u003C/a>\u003C/li>\n\u003Cli>Importieren, ggf. Netzwerkbrücke und Shared Folders aktivieren\u003C/li>\n\u003C/ul>\n\u003Ch2 id=\"konfiguration\">Konfiguration\u003C/h2>\n\u003Ch3 id=\"netzwerkeinstellungen\">Netzwerkeinstellungen\u003C/h3>\n\u003Cul>\n\u003Cli>Konfiguration über \u003Ccode>nmtui\u003C/code> oder \u003Ccode>/etc/network/interfaces\u003C/code>\u003C/li>\n\u003Cli>VPN und Proxy-Integration über GUI oder Terminal\u003C/li>\n\u003C/ul>\n\u003Ch3 id=\"updates--paketquellen\">Updates & Paketquellen\u003C/h3>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"bash\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">sudo\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> apt\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> update\u003C/span>\u003Cspan style=\"color:#E1E4E8\"> && \u003C/span>\u003Cspan style=\"color:#B392F0\">sudo\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> apt\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> full-upgrade\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Cblockquote>\n\u003Cp>Hinweis: \u003Ccode>kali-rolling\u003C/code> ist die Standard-Distribution für kontinuierliche Updates.\u003C/p>\n\u003C/blockquote>\n\u003Ch3 id=\"sprache--lokalisierung\">Sprache & Lokalisierung\u003C/h3>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"bash\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">sudo\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> dpkg-reconfigure\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> locales\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">sudo\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> dpkg-reconfigure\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> keyboard-configuration\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Ch2 id=\"verwendungsbeispiele\">Verwendungsbeispiele\u003C/h2>\n\u003Ch3 id=\"1-netzwerkscan-mit-nmap\">1. Netzwerkscan mit Nmap\u003C/h3>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"bash\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">nmap\u003C/span>\u003Cspan style=\"color:#79B8FF\"> -sS\u003C/span>\u003Cspan style=\"color:#79B8FF\"> -T4\u003C/span>\u003Cspan style=\"color:#79B8FF\"> -A\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> 192.168.1.0/24\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Ch3 id=\"2-passwort-cracking-mit-john-the-ripper\">2. Passwort-Cracking mit John the Ripper\u003C/h3>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"bash\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">john\u003C/span>\u003Cspan style=\"color:#79B8FF\"> --wordlist=/usr/share/wordlists/rockyou.txt\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> hashes.txt\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Ch3 id=\"3-forensik-mit-autopsy\">3. Forensik mit Autopsy\u003C/h3>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"bash\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">autopsy\u003C/span>\u003Cspan style=\"color:#E1E4E8\"> &\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Ch3 id=\"4-android-analyse-mit-mobsf-in-docker\">4. Android-Analyse mit MobSF (in Docker)\u003C/h3>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"bash\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">docker\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> pull\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> opensecurity/mobile-security-framework-mobsf\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">docker\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> run\u003C/span>\u003Cspan style=\"color:#79B8FF\"> -it\u003C/span>\u003Cspan style=\"color:#79B8FF\"> -p\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> 8000:8000\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> mobsf\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Ch2 id=\"best-practices\">Best Practices\u003C/h2>\n\u003Cul>\n\u003Cli>Nutze immer \u003Cstrong>aktuelle Snapshots\u003C/strong> oder VM-Clones vor gefährlichen Tests\u003C/li>\n\u003Cli>Verwende separate Netzwerke (z. B. Host-only oder NAT) für Tests\u003C/li>\n\u003Cli>Deaktiviere automatisches WLAN bei forensischen Analysen\u003C/li>\n\u003Cli>Prüfe und aktualisiere regelmäßig Toolsets (\u003Ccode>apt\u003C/code>, \u003Ccode>git\u003C/code>, \u003Ccode>pip\u003C/code>)\u003C/li>\n\u003Cli>Halte deine ISO-Images versioniert für forensische Reproduzierbarkeit\u003C/li>\n\u003C/ul>\n\u003Ch2 id=\"troubleshooting\">Troubleshooting\u003C/h2>\n\u003Ch3 id=\"problem-keine-internetverbindung-nach-installation\">Problem: Keine Internetverbindung nach Installation\u003C/h3>\n\u003Cp>\u003Cstrong>Lösung:\u003C/strong> Netzwerkadapter prüfen, ggf. mit \u003Ccode>ifconfig\u003C/code> oder \u003Ccode>ip a\u003C/code> überprüfen, DHCP aktivieren.\u003C/p>\n\u003Ch3 id=\"problem-tools-fehlen-nach-update\">Problem: Tools fehlen nach Update\u003C/h3>\n\u003Cp>\u003Cstrong>Lösung:\u003C/strong> Tool-Gruppen wie \u003Ccode>kali-linux-default\u003C/code> manuell nachinstallieren:\u003C/p>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"bash\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">sudo\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> apt\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> install\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> kali-linux-default\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Ch3 id=\"problem-permission-denied-bei-tools\">Problem: „Permission Denied“ bei Tools\u003C/h3>\n\u003Cp>\u003Cstrong>Lösung:\u003C/strong> Root-Rechte nutzen oder mit \u003Ccode>sudo\u003C/code> ausführen.\u003C/p>\n\u003Ch2 id=\"weiterführende-themen\">Weiterführende Themen\u003C/h2>\n\u003Cul>\n\u003Cli>\u003Cstrong>Kustomisierung von Kali ISOs\u003C/strong> mit \u003Ccode>live-build\u003C/code>\u003C/li>\n\u003Cli>\u003Cstrong>NetHunter\u003C/strong>: Kali für mobile Geräte (Android)\u003C/li>\n\u003Cli>\u003Cstrong>Kali Purple\u003C/strong>: Defensive Security Suite\u003C/li>\n\u003Cli>Integration mit \u003Cstrong>Cloud-Infrastrukturen\u003C/strong> via WSL oder Azure\u003C/li>\n\u003C/ul>\n\u003Chr>\n\u003Cp>\u003Cstrong>Links & Ressourcen:\u003C/strong>\u003C/p>\n\u003Cul>\n\u003Cli>Offizielle Website: \u003Ca href=\"https://kali.org/\">https://kali.org\u003C/a>\u003C/li>\n\u003Cli>Dokumentation: \u003Ca href=\"https://docs.kali.org/\">https://docs.kali.org/\u003C/a>\u003C/li>\n\u003Cli>GitLab Repo: \u003Ca href=\"https://gitlab.com/kalilinux\">https://gitlab.com/kalilinux\u003C/a>\u003C/li>\n\u003Cli>Discord-Community: \u003Ca href=\"https://discord.com/invite/kali-linux\">https://discord.com/invite/kali-linux\u003C/a>\u003C/li>\n\u003C/ul>",{"headings":172,"localImagePaths":219,"remoteImagePaths":220,"frontmatter":151,"imagePaths":221},[173,174,175,178,181,184,185,188,191,194,195,198,201,204,207,208,209,212,215,218],{"depth":41,"slug":42,"text":43},{"depth":45,"slug":104,"text":105},{"depth":49,"slug":176,"text":177},"option-1-live-system-usbdvd","Option 1: Live-System (USB/DVD)",{"depth":49,"slug":179,"text":180},"option-2-installation-auf-festplatte","Option 2: Installation auf Festplatte",{"depth":49,"slug":182,"text":183},"option-3-virtuelle-maschine-vm","Option 3: Virtuelle Maschine (VM)",{"depth":45,"slug":113,"text":114},{"depth":49,"slug":186,"text":187},"netzwerkeinstellungen","Netzwerkeinstellungen",{"depth":49,"slug":189,"text":190},"updates--paketquellen","Updates & Paketquellen",{"depth":49,"slug":192,"text":193},"sprache--lokalisierung","Sprache & Lokalisierung",{"depth":45,"slug":46,"text":47},{"depth":49,"slug":196,"text":197},"1-netzwerkscan-mit-nmap","1. Netzwerkscan mit Nmap",{"depth":49,"slug":199,"text":200},"2-passwort-cracking-mit-john-the-ripper","2. Passwort-Cracking mit John the Ripper",{"depth":49,"slug":202,"text":203},"3-forensik-mit-autopsy","3. Forensik mit Autopsy",{"depth":49,"slug":205,"text":206},"4-android-analyse-mit-mobsf-in-docker","4. Android-Analyse mit MobSF (in Docker)",{"depth":45,"slug":62,"text":63},{"depth":45,"slug":133,"text":134},{"depth":49,"slug":210,"text":211},"problem-keine-internetverbindung-nach-installation","Problem: Keine Internetverbindung nach Installation",{"depth":49,"slug":213,"text":214},"problem-tools-fehlen-nach-update","Problem: Tools fehlen nach Update",{"depth":49,"slug":216,"text":217},"problem-permission-denied-bei-tools","Problem: „Permission Denied“ bei Tools",{"depth":45,"slug":65,"text":66},[],[],[],"kali-linux.md","velociraptor",{"id":223,"data":225,"body":239,"filePath":240,"digest":241,"rendered":242,"legacyId":287},{"title":226,"tool_name":227,"description":228,"last_updated":229,"author":16,"difficulty":230,"categories":231,"tags":232,"sections":238,"review_status":32},"Velociraptor – Skalierbare Endpoint-Forensik mit VQL","Velociraptor","Detaillierte Anleitung und Best Practices für Velociraptor – Remote-Forensik der nächsten Generation",["Date","2025-07-20T00:00:00.000Z"],"advanced",[19,20,21],[88,233,234,235,236,237],"endpoint-monitoring","artifact-extraction","scripting","live-forensics","hunting",{"overview":30,"installation":30,"configuration":30,"usage_examples":30,"best_practices":30,"troubleshooting":30,"advanced_topics":30},"> **⚠️ Hinweis**: Dies ist ein vorläufiger, KI-generierter Knowledgebase-Eintrag. Wir freuen uns über Verbesserungen und Ergänzungen durch die Community!\n\n\n# Übersicht\n\nVelociraptor ist ein Open-Source-Tool zur Endpoint-Forensik mit Fokus auf Skalierbarkeit, Präzision und Geschwindigkeit. Es ermöglicht die zielgerichtete Erfassung und Analyse digitaler Artefakte über eine eigene Query Language – VQL (Velociraptor Query Language). Die Architektur erlaubt remote Zugriff auf tausende Endpoints gleichzeitig, ohne dass vollständige Disk-Images erforderlich sind.\n\n## Hauptmerkmale\n\n- 🌐 Web-basierte Benutzeroberfläche\n- 💡 VQL – mächtige, SQL-ähnliche Abfragesprache\n- 🚀 Hochskalierbare Hunt-Funktionalität\n- 🔍 Artefaktbasierte Sammlung (ohne Full-Image)\n- 🖥️ Plattformunterstützung für Windows, macOS, Linux\n- 📦 Apache 2.0 Lizenz – Open Source\n\nWeitere Infos: [velociraptor.app](https://www.velociraptor.app/)  \nProjektspiegel: [raptor.cc24.dev](https://raptor.cc24.dev)  \nStatus: ![Status](https://status.mikoshi.de/api/badge/33/status)\n\n---\n\n## Installation\n\n### Voraussetzungen\n\n- Python ≥ 3.9\n- Adminrechte auf dem System\n- Firewall-Freigaben für Webport (Standard: 8000)\n\n### Installation unter Linux/macOS\n\n```bash\nwget https://github.com/Velocidex/velociraptor/releases/latest/download/velociraptor\nchmod +x velociraptor\nsudo mv velociraptor /usr/local/bin/\n````\n\n### Installation unter Windows\n\n1. Download der `.exe` von der [Release-Seite](https://github.com/Velocidex/velociraptor/releases)\n2. Ausführung in PowerShell mit Adminrechten:\n\n   ```powershell\n   .\\velociraptor.exe config generate > server.config.yaml\n   ```\n\n---\n\n## Konfiguration\n\n### Server Setup\n\n1. Generiere die Konfigurationsdatei:\n\n   ```bash\n   velociraptor config generate > server.config.yaml\n   ```\n2. Starte den Server:\n\n   ```bash\n   velociraptor --config server.config.yaml frontend\n   ```\n3. Zugriff über Browser via `https://\u003Chostname>:8000`\n\n### Client Deployment\n\n* MSI/EXE für Windows, oder `deb/rpm` für Linux\n* Unterstützt automatische Registrierung am Server\n* Deployment über GPO, Puppet, Ansible etc. möglich\n\n---\n\n## Verwendungsbeispiele\n\n### 1. Live-Memory-Artefakte sammeln\n\n```vql\nSELECT * FROM Artifact.MemoryInfo()\n```\n\n### 2. Hunt starten auf verdächtige Prozesse\n\n```vql\nSELECT * FROM pslist()\nWHERE Name =~ \"mimikatz|cobaltstrike\"\n```\n\n### 3. Dateiinhalt extrahieren\n\n```vql\nSELECT * FROM glob(globs=\"C:\\\\Users\\\\*\\\\AppData\\\\*.dat\")\n```\n\n---\n\n## Best Practices\n\n* Erstelle eigene Artefakte für unternehmensspezifische Bedrohungsmodelle\n* Verwende \"Notebook\"-Funktion für strukturierte Analysen\n* Nutze \"Labels\", um Endpoints zu organisieren (z. B. `location:Berlin`)\n* Kombiniere Velociraptor mit SIEM/EDR-Systemen über REST API\n\n---\n\n## Troubleshooting\n\n### Problem: Keine Verbindung vom Client zum Server\n\n**Lösung:**\n\n* Ports freigegeben? (Default: 8000/tcp)\n* TLS-Zertifikate korrekt generiert?\n* `server.config.yaml` auf korrekte `public_ip` prüfen\n\n### Problem: Hunt hängt in Warteschleife\n\n**Lösung:**\n\n* Genügend Worker-Prozesse aktiv?\n* Endpoint online?\n* `log_level` auf `debug` setzen und Log analysieren\n\n---\n\n## Weiterführende Themen\n\n* Eigene Artefakte schreiben mit VQL\n* Integration mit ELK Stack\n* Automatisiertes Incident Response Playbook\n* Velociraptor als IR-as-a-Service einsetzen\n\n---\n\n🧠 **Tipp:** Die Lernkurve bei VQL ist steil – aber mit hohem ROI. Testumgebung aufsetzen und mit Community-Artefakten starten.\n\n📚 Weitere Ressourcen:\n\n* [Offizielle Doku](https://docs.velociraptor.app/)\n* [YouTube Channel](https://www.youtube.com/c/VelociraptorDFIR)\n* [Community auf Discord](https://www.velociraptor.app/community/)","src/content/knowledgebase/velociraptor.md","05636b9b97e61d17",{"html":243,"metadata":244},"\u003Cblockquote>\n\u003Cp>\u003Cstrong>⚠️ Hinweis\u003C/strong>: Dies ist ein vorläufiger, KI-generierter Knowledgebase-Eintrag. Wir freuen uns über Verbesserungen und Ergänzungen durch die Community!\u003C/p>\n\u003C/blockquote>\n\u003Ch1 id=\"übersicht\">Übersicht\u003C/h1>\n\u003Cp>Velociraptor ist ein Open-Source-Tool zur Endpoint-Forensik mit Fokus auf Skalierbarkeit, Präzision und Geschwindigkeit. Es ermöglicht die zielgerichtete Erfassung und Analyse digitaler Artefakte über eine eigene Query Language – VQL (Velociraptor Query Language). Die Architektur erlaubt remote Zugriff auf tausende Endpoints gleichzeitig, ohne dass vollständige Disk-Images erforderlich sind.\u003C/p>\n\u003Ch2 id=\"hauptmerkmale\">Hauptmerkmale\u003C/h2>\n\u003Cul>\n\u003Cli>🌐 Web-basierte Benutzeroberfläche\u003C/li>\n\u003Cli>💡 VQL – mächtige, SQL-ähnliche Abfragesprache\u003C/li>\n\u003Cli>🚀 Hochskalierbare Hunt-Funktionalität\u003C/li>\n\u003Cli>🔍 Artefaktbasierte Sammlung (ohne Full-Image)\u003C/li>\n\u003Cli>🖥️ Plattformunterstützung für Windows, macOS, Linux\u003C/li>\n\u003Cli>📦 Apache 2.0 Lizenz – Open Source\u003C/li>\n\u003C/ul>\n\u003Cp>Weitere Infos: \u003Ca href=\"https://www.velociraptor.app/\">velociraptor.app\u003C/a>\u003Cbr>\nProjektspiegel: \u003Ca href=\"https://raptor.cc24.dev\">raptor.cc24.dev\u003C/a>\u003Cbr>\nStatus: \u003Cimg src=\"https://status.mikoshi.de/api/badge/33/status\" alt=\"Status\">\u003C/p>\n\u003Chr>\n\u003Ch2 id=\"installation\">Installation\u003C/h2>\n\u003Ch3 id=\"voraussetzungen\">Voraussetzungen\u003C/h3>\n\u003Cul>\n\u003Cli>Python ≥ 3.9\u003C/li>\n\u003Cli>Adminrechte auf dem System\u003C/li>\n\u003Cli>Firewall-Freigaben für Webport (Standard: 8000)\u003C/li>\n\u003C/ul>\n\u003Ch3 id=\"installation-unter-linuxmacos\">Installation unter Linux/macOS\u003C/h3>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"bash\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">wget\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> https://github.com/Velocidex/velociraptor/releases/latest/download/velociraptor\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">chmod\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> +x\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> velociraptor\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">sudo\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> mv\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> velociraptor\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> /usr/local/bin/\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Ch3 id=\"installation-unter-windows\">Installation unter Windows\u003C/h3>\n\u003Col>\n\u003Cli>\n\u003Cp>Download der \u003Ccode>.exe\u003C/code> von der \u003Ca href=\"https://github.com/Velocidex/velociraptor/releases\">Release-Seite\u003C/a>\u003C/p>\n\u003C/li>\n\u003Cli>\n\u003Cp>Ausführung in PowerShell mit Adminrechten:\u003C/p>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"powershell\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#E1E4E8\">.\\\u003C/span>\u003Cspan style=\"color:#79B8FF\">velociraptor.exe\u003C/span>\u003Cspan style=\"color:#E1E4E8\"> config generate \u003C/span>\u003Cspan style=\"color:#F97583\">>\u003C/span>\u003Cspan style=\"color:#E1E4E8\"> server.config.yaml\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003C/li>\n\u003C/ol>\n\u003Chr>\n\u003Ch2 id=\"konfiguration\">Konfiguration\u003C/h2>\n\u003Ch3 id=\"server-setup\">Server Setup\u003C/h3>\n\u003Col>\n\u003Cli>\n\u003Cp>Generiere die Konfigurationsdatei:\u003C/p>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"bash\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">velociraptor\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> config\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> generate\u003C/span>\u003Cspan style=\"color:#F97583\"> >\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> server.config.yaml\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003C/li>\n\u003Cli>\n\u003Cp>Starte den Server:\u003C/p>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"bash\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">velociraptor\u003C/span>\u003Cspan style=\"color:#79B8FF\"> --config\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> server.config.yaml\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> frontend\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003C/li>\n\u003Cli>\n\u003Cp>Zugriff über Browser via \u003Ccode>https://<hostname>:8000\u003C/code>\u003C/p>\n\u003C/li>\n\u003C/ol>\n\u003Ch3 id=\"client-deployment\">Client Deployment\u003C/h3>\n\u003Cul>\n\u003Cli>MSI/EXE für Windows, oder \u003Ccode>deb/rpm\u003C/code> für Linux\u003C/li>\n\u003Cli>Unterstützt automatische Registrierung am Server\u003C/li>\n\u003Cli>Deployment über GPO, Puppet, Ansible etc. möglich\u003C/li>\n\u003C/ul>\n\u003Chr>\n\u003Ch2 id=\"verwendungsbeispiele\">Verwendungsbeispiele\u003C/h2>\n\u003Ch3 id=\"1-live-memory-artefakte-sammeln\">1. Live-Memory-Artefakte sammeln\u003C/h3>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"plaintext\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan>SELECT * FROM Artifact.MemoryInfo()\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Ch3 id=\"2-hunt-starten-auf-verdächtige-prozesse\">2. Hunt starten auf verdächtige Prozesse\u003C/h3>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"plaintext\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan>SELECT * FROM pslist()\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan>WHERE Name =~ \"mimikatz|cobaltstrike\"\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Ch3 id=\"3-dateiinhalt-extrahieren\">3. Dateiinhalt extrahieren\u003C/h3>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"plaintext\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan>SELECT * FROM glob(globs=\"C:\\\\Users\\\\*\\\\AppData\\\\*.dat\")\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Chr>\n\u003Ch2 id=\"best-practices\">Best Practices\u003C/h2>\n\u003Cul>\n\u003Cli>Erstelle eigene Artefakte für unternehmensspezifische Bedrohungsmodelle\u003C/li>\n\u003Cli>Verwende “Notebook”-Funktion für strukturierte Analysen\u003C/li>\n\u003Cli>Nutze “Labels”, um Endpoints zu organisieren (z. B. \u003Ccode>location:Berlin\u003C/code>)\u003C/li>\n\u003Cli>Kombiniere Velociraptor mit SIEM/EDR-Systemen über REST API\u003C/li>\n\u003C/ul>\n\u003Chr>\n\u003Ch2 id=\"troubleshooting\">Troubleshooting\u003C/h2>\n\u003Ch3 id=\"problem-keine-verbindung-vom-client-zum-server\">Problem: Keine Verbindung vom Client zum Server\u003C/h3>\n\u003Cp>\u003Cstrong>Lösung:\u003C/strong>\u003C/p>\n\u003Cul>\n\u003Cli>Ports freigegeben? (Default: 8000/tcp)\u003C/li>\n\u003Cli>TLS-Zertifikate korrekt generiert?\u003C/li>\n\u003Cli>\u003Ccode>server.config.yaml\u003C/code> auf korrekte \u003Ccode>public_ip\u003C/code> prüfen\u003C/li>\n\u003C/ul>\n\u003Ch3 id=\"problem-hunt-hängt-in-warteschleife\">Problem: Hunt hängt in Warteschleife\u003C/h3>\n\u003Cp>\u003Cstrong>Lösung:\u003C/strong>\u003C/p>\n\u003Cul>\n\u003Cli>Genügend Worker-Prozesse aktiv?\u003C/li>\n\u003Cli>Endpoint online?\u003C/li>\n\u003Cli>\u003Ccode>log_level\u003C/code> auf \u003Ccode>debug\u003C/code> setzen und Log analysieren\u003C/li>\n\u003C/ul>\n\u003Chr>\n\u003Ch2 id=\"weiterführende-themen\">Weiterführende Themen\u003C/h2>\n\u003Cul>\n\u003Cli>Eigene Artefakte schreiben mit VQL\u003C/li>\n\u003Cli>Integration mit ELK Stack\u003C/li>\n\u003Cli>Automatisiertes Incident Response Playbook\u003C/li>\n\u003Cli>Velociraptor als IR-as-a-Service einsetzen\u003C/li>\n\u003C/ul>\n\u003Chr>\n\u003Cp>🧠 \u003Cstrong>Tipp:\u003C/strong> Die Lernkurve bei VQL ist steil – aber mit hohem ROI. Testumgebung aufsetzen und mit Community-Artefakten starten.\u003C/p>\n\u003Cp>📚 Weitere Ressourcen:\u003C/p>\n\u003Cul>\n\u003Cli>\u003Ca href=\"https://docs.velociraptor.app/\">Offizielle Doku\u003C/a>\u003C/li>\n\u003Cli>\u003Ca href=\"https://www.youtube.com/c/VelociraptorDFIR\">YouTube Channel\u003C/a>\u003C/li>\n\u003Cli>\u003Ca href=\"https://www.velociraptor.app/community/\">Community auf Discord\u003C/a>\u003C/li>\n\u003C/ul>",{"headings":245,"localImagePaths":284,"remoteImagePaths":285,"frontmatter":225,"imagePaths":286},[246,247,250,251,252,255,258,259,262,265,266,269,272,275,276,277,280,283],{"depth":41,"slug":42,"text":43},{"depth":45,"slug":248,"text":249},"hauptmerkmale","Hauptmerkmale",{"depth":45,"slug":104,"text":105},{"depth":49,"slug":107,"text":108},{"depth":49,"slug":253,"text":254},"installation-unter-linuxmacos","Installation unter Linux/macOS",{"depth":49,"slug":256,"text":257},"installation-unter-windows","Installation unter Windows",{"depth":45,"slug":113,"text":114},{"depth":49,"slug":260,"text":261},"server-setup","Server Setup",{"depth":49,"slug":263,"text":264},"client-deployment","Client Deployment",{"depth":45,"slug":46,"text":47},{"depth":49,"slug":267,"text":268},"1-live-memory-artefakte-sammeln","1. Live-Memory-Artefakte sammeln",{"depth":49,"slug":270,"text":271},"2-hunt-starten-auf-verdächtige-prozesse","2. Hunt starten auf verdächtige Prozesse",{"depth":49,"slug":273,"text":274},"3-dateiinhalt-extrahieren","3. Dateiinhalt extrahieren",{"depth":45,"slug":62,"text":63},{"depth":45,"slug":133,"text":134},{"depth":49,"slug":278,"text":279},"problem-keine-verbindung-vom-client-zum-server","Problem: Keine Verbindung vom Client zum Server",{"depth":49,"slug":281,"text":282},"problem-hunt-hängt-in-warteschleife","Problem: Hunt hängt in Warteschleife",{"depth":45,"slug":65,"text":66},[],[],[],"velociraptor.md","android-logical-imaging",{"id":288,"data":290,"body":303,"filePath":304,"digest":305,"rendered":306,"legacyId":534},{"title":291,"tool_name":292,"description":293,"last_updated":294,"author":295,"difficulty":230,"categories":296,"tags":298,"sections":302,"review_status":32},"Extraktion logischer Dateisysteme alter Android-Smartphones - eine KI-Recherche","Android Logical Imaging","Wie man alte Android-Handys aufbekommen könnte - eine Recherche von Claude",["Date","2025-07-21T00:00:00.000Z"],"Claude 4 Sonnet (Research)",[297],"data-collection",[299,300,301],"imaging","filesystem","hardware-interface",{"overview":30,"installation":30,"configuration":30,"usage_examples":30,"best_practices":30,"troubleshooting":30,"advanced_topics":30},"# Übersicht\n\nOpen-Source Android Forensik bietet robuste Alternativen zu kommerziellen Lösungen wie Cellebrite UFED und Magnet AXIOM. Besonders für ältere Android-Geräte (5+ Jahre) existieren bewährte Methoden zur Datenextraktion und -analyse.\n\n## Kernkomponenten des Open-Source Forensik-Stacks\n\n**Autopsy Digital Forensics Platform** bildet das Fundament mit GUI-basierter Analyse und integrierten Android-Parsing-Fähigkeiten. Die Plattform unterstützt **ALEAPP (Android Logs Events And Protobuf Parser)**, das über 100 Artefakt-Kategorien aus Android-Extraktionen parst.\n\n**Mobile Verification Toolkit (MVT)** von Amnesty International bietet spezialisierte Command-Line-Tools für Android-Analyse mit Fokus auf Kompromittierungserkennung.\n\n**SIFT Workstation** stellt eine komplette Ubuntu-basierte forensische Umgebung mit 125+ vorinstallierten Tools bereit.\n\n## Erfolgsraten nach Gerätealter\n\n- **Pre-2017 Geräte**: 85-98% logische Extraktion, 30-70% physische Extraktion\n- **2017-2019 Geräte**: 80-95% logische Extraktion, 15-35% physische Extraktion  \n- **2020+ Geräte**: 70-85% logische Extraktion, 5-15% physische Extraktion\n\n# Installation\n\n## SIFT Workstation Setup\n\n### Systemanforderungen\n- Quad-Core CPU 2.5GHz+\n- 16GB+ RAM\n- 500GB+ SSD Speicher\n- USB 3.0+ Anschlüsse\n\n### Installation\n1. Download von [SANS SIFT Workstation](https://www.sans.org/tools/sift-workstation/)\n2. VMware/VirtualBox Import der OVA-Datei\n3. VM-Konfiguration: 8GB+ RAM, 4+ CPU-Kerne\n\n```bash\n# Update nach Installation\nsudo apt update && sudo apt upgrade -y\nsudo sift update\n```\n\n## Autopsy Installation\n\n### Windows Installation\n1. Download von [autopsy.com](https://www.autopsy.com/)\n2. Java 8+ Installation erforderlich\n3. Installation mit Administratorrechten\n\n### Linux Installation\n```bash\n# Ubuntu/Debian\nsudo apt install autopsy sleuthkit\n# Oder manueller Download und Installation\nwget https://github.com/sleuthkit/autopsy/releases/latest\n```\n\n## Essential Tools Installation\n\n### Android Debug Bridge (ADB)\n```bash\n# Ubuntu/Debian\nsudo apt install android-tools-adb android-tools-fastboot\n\n# Windows - Download Android Platform Tools\n# https://developer.android.com/studio/releases/platform-tools\n```\n\n### ALEAPP Installation\n```bash\ngit clone https://github.com/abrignoni/ALEAPP.git\ncd ALEAPP\npip3 install -r requirements.txt\n```\n\n### Mobile Verification Toolkit (MVT)\n```bash\npip3 install mvt\n# Oder via GitHub für neueste Version\ngit clone https://github.com/mvt-project/mvt.git\ncd mvt && pip3 install .\n```\n\n### Andriller Installation\n```bash\ngit clone https://github.com/den4uk/andriller.git\ncd andriller\npip3 install -r requirements.txt\n```\n\n# Konfiguration\n\n## ADB Setup und Gerätevorbereitung\n\n### USB-Debugging aktivieren\n1. Entwickleroptionen freischalten (7x Build-Nummer antippen)\n2. USB-Debugging aktivieren\n3. Gerät via USB verbinden\n4. RSA-Fingerprint akzeptieren\n\n### ADB Verbindung testen\n```bash\nadb devices\n# Sollte Gerät mit \"device\" Status zeigen\nadb shell getprop ro.build.version.release  # Android Version\nadb shell getprop ro.product.model         # Gerätemodell\n```\n\n## Autopsy Projektkonfiguration\n\n### Case-Setup\n1. Neuen Fall erstellen\n2. Ermittler-Informationen eingeben\n3. Case-Verzeichnis festlegen (ausreichend Speicherplatz)\n\n### Android Analyzer Module aktivieren\n- Tools → Options → Modules\n- Android Analyzer aktivieren\n- ALEAPP Integration konfigurieren\n\n### Hash-Algorithmen konfigurieren\n- MD5, SHA-1, SHA-256 für Integritätsprüfung\n- Automatische Hash-Berechnung bei Import aktivieren\n\n## MVT Konfiguration\n\n### Konfigurationsdatei erstellen\n```yaml\n# ~/.mvt/config.yaml\nadb_path: \"/usr/bin/adb\"\noutput_folder: \"/home/user/mvt_output\"\n```\n\n# Verwendungsbeispiele\n\n## Fall 1: Logische Datenextraktion mit ADB\n\n### Geräteinformationen sammeln\n```bash\n# Systeminfo\nadb shell getprop > device_properties.txt\nadb shell cat /proc/version > kernel_info.txt\nadb shell mount > mount_info.txt\n\n# Installierte Apps\nadb shell pm list packages -f > installed_packages.txt\n```\n\n### Datenbank-Extraktion\n```bash\n# SMS/MMS Datenbank\nadb pull /data/data/com.android.providers.telephony/databases/mmssms.db\n\n# Kontakte\nadb pull /data/data/com.android.providers.contacts/databases/contacts2.db\n\n# Anrufliste  \nadb pull /data/data/com.android.providers.contacts/databases/calllog.db\n```\n\n### WhatsApp Datenextraktion\n```bash\n# WhatsApp Datenbanken (Root erforderlich)\nadb shell su -c \"cp -r /data/data/com.whatsapp/ /sdcard/whatsapp_backup/\"\nadb pull /sdcard/whatsapp_backup/\n```\n\n## Fall 2: Android Backup-Analyse\n\n### Vollständiges Backup erstellen\n```bash\n# Umfassendes Backup (ohne Root)\nadb backup -all -system -apk -shared -f backup.ab\n\n# Backup entschlüsseln (falls verschlüsselt)\njava -jar abe.jar unpack backup.ab backup.tar\ntar -xf backup.tar\n```\n\n### Backup mit ALEAPP analysieren\n```bash\npython3 aleappGUI.py\n# Oder Command-Line\npython3 aleapp.py -t tar -i backup.tar -o output_folder\n```\n\n## Fall 3: MVT Kompromittierungsanalyse\n\n### Live-Geräteanalyse\n```bash\n# ADB-basierte Analyse\nmvt-android check-adb --output /path/to/output/\n\n# Backup-Analyse\nmvt-android check-backup --output /path/to/output/ backup.ab\n```\n\n### IOC-Suche mit Pegasus-Indikatoren\n```bash\n# Mit vorgefertigten IOCs\nmvt-android check-adb --iocs /path/to/pegasus.stix2 --output results/\n```\n\n## Fall 4: Physische Extraktion (Root erforderlich)\n\n### Device Rooting - MediaTek Geräte\n```bash\n# MTKClient für MediaTek-Chipsets\ngit clone https://github.com/bkerler/mtkclient.git\ncd mtkclient\npython3 mtk payload\n\n# Nach erfolgreichem Root\nadb shell su\n```\n\n### Vollständiges Memory Dump\n```bash\n# Partitionslayout ermitteln\nadb shell su -c \"cat /proc/partitions\"\nadb shell su -c \"ls -la /dev/block/\"\n\n# Vollständiges Device Image (Root erforderlich)\nadb shell su -c \"dd if=/dev/block/mmcblk0 of=/sdcard/full_device.img bs=4096\"\nadb pull /sdcard/full_device.img\n```\n\n# Best Practices\n\n## Rechtliche Compliance\n\n### Dokumentation und Chain of Custody\n- **Vollständige Dokumentation**: Wer, Was, Wann, Wo, Warum\n- **Hash-Verifikation**: MD5/SHA-256 für alle extrahierten Daten\n- **Nur forensische Kopien analysieren**, niemals Originaldaten\n- **Schriftliche Genehmigung** für Geräteanalyse einholen\n\n### Familiengeräte und Nachlässe\n- Genehmigung durch Nachlassverwalter erforderlich\n- Gerichtsbeschlüsse für Cloud-Zugang eventuell nötig\n- Drittpartei-Kommunikation kann weiterhin geschützt sein\n\n## Technische Best Practices\n\n### Hash-Integrität sicherstellen\n```bash\n# Hash vor und nach Transfer prüfen\nmd5sum original_file.db\nsha256sum original_file.db\n\n# Hash-Verifikation dokumentieren\necho \"$(date): MD5: $(md5sum file.db)\" >> chain_of_custody.log\n```\n\n### Sichere Arbeitsumgebung\n- Isolierte VM für Forensik-Arbeit\n- Netzwerk-Isolation während Analyse\n- Verschlüsselte Speicherung aller Evidenz\n- Regelmäßige Backups der Case-Datenbanken\n\n### Qualitätssicherung\n- Peer-Review kritischer Analysen\n- Standardisierte Arbeitsabläufe (SOPs)\n- Regelmäßige Tool-Validierung\n- Kontinuierliche Weiterbildung\n\n## Erfolgsmaximierung nach Gerätehersteller\n\n### MediaTek-Geräte (Höchste Erfolgsrate)\n- BootROM-Exploits für MT6735, MT6737, MT6750, MT6753, MT6797\n- MTKClient für Hardware-Level-Zugang\n- Erfolgsrate: 80%+ für Geräte 2015-2019\n\n### Samsung-Geräte\n- Ältere Knox-Implementierungen umgehbar\n- Emergency Dialer Exploits für Android 4.x\n- Erfolgsrate: 40-70% je nach Knox-Version\n\n### Pixel/Nexus-Geräte\n- Bootloader-Unlocking oft möglich\n- Fastboot-basierte Recovery-Installation\n- Erfolgsrate: 60-80% bei freigeschaltetem Bootloader\n\n# Troubleshooting\n\n## Problem: ADB erkennt Gerät nicht\n\n### Lösung: USB-Treiber und Berechtigungen\n```bash\n# Linux: USB-Berechtigungen prüfen\nlsusb | grep -i android\nsudo chmod 666 /dev/bus/usb/XXX/XXX\n\n# udev-Regeln erstellen\necho 'SUBSYSTEM==\"usb\", ATTR{idVendor}==\"18d1\", MODE=\"0666\", GROUP=\"plugdev\"' | sudo tee /etc/udev/rules.d/51-android.rules\nsudo udevadm control --reload-rules\n```\n\n### Windows: Treiber-Installation\n1. Geräte-Manager öffnen\n2. Android-Gerät mit Warnsymbol finden\n3. Treiber manuell installieren (Android USB Driver)\n\n## Problem: Verschlüsselte Android Backups\n\n### Lösung: Android Backup Extractor\n```bash\n# ADB Backup Extractor installieren\ngit clone https://github.com/nelenkov/android-backup-extractor.git\ncd android-backup-extractor\ngradle build\n\n# Backup entschlüsseln\njava -jar abe.jar unpack backup.ab backup.tar [password]\n```\n\n## Problem: Unzureichende Berechtigungen für Datenextraktion\n\n### Lösung: Alternative Extraktionsmethoden\n```bash\n# AFLogical OSE für begrenzte Extraktion ohne Root\n# WhatsApp Key/DB Extractor für spezifische Apps\n# Backup-basierte Extraktion als Fallback\n\n# Custom Recovery für erweiterten Zugang\nfastboot flash recovery twrp-device.img\n```\n\n## Problem: ALEAPP Parsing-Fehler\n\n### Lösung: Datenformat-Probleme beheben\n```bash\n# Log-Dateien prüfen\npython3 aleapp.py -t dir -i /path/to/data -o output --debug\n\n# Spezifische Parser deaktivieren\n# Manuelle SQLite-Analyse bei Parser-Fehlern\nsqlite3 database.db \".tables\"\nsqlite3 database.db \".schema table_name\"\n```\n\n# Erweiterte Techniken\n\n## Memory Forensics mit LiME\n\n### LiME für ARM-Devices kompilieren\n```bash\n# Cross-Compilation Setup\nexport ARCH=arm\nexport CROSS_COMPILE=arm-linux-gnueabi-\nexport KERNEL_DIR=/path/to/kernel/source\n\n# LiME Module kompilieren\ngit clone https://github.com/504ensicsLabs/LiME.git\ncd LiME/src\nmake\n\n# Memory Dump erstellen (Root erforderlich)\nadb push lime.ko /data/local/tmp/\nadb shell su -c \"insmod /data/local/tmp/lime.ko 'path=/sdcard/memory.lime format=lime'\"\n```\n\n### Volatility-Analyse von Android Memory\n```bash\n# Memory Dump analysieren\npython vol.py -f memory.lime --profile=Linux \u003Cprofile> linux.pslist\npython vol.py -f memory.lime --profile=Linux \u003Cprofile> linux.bash\npython vol.py -f memory.lime --profile=Linux \u003Cprofile> linux.netstat\n```\n\n## FRIDA-basierte Runtime-Analyse\n\n### FRIDA für Kryptographie-Hooks\n```javascript\n// crypto_hooks.js - SSL/TLS Traffic abfangen\nJava.perform(function() {\n    var SSLContext = Java.use(\"javax.net.ssl.SSLContext\");\n    SSLContext.init.overload('[Ljavax.net.ssl.KeyManager;', '[Ljavax.net.ssl.TrustManager;', 'java.security.SecureRandom').implementation = function(keyManagers, trustManagers, secureRandom) {\n        console.log(\"[+] SSLContext.init() called\");\n        this.init(keyManagers, trustManagers, secureRandom);\n    };\n});\n```\n\n### FRIDA Installation und Verwendung\n```bash\n# FRIDA Server auf Android-Gerät installieren\nadb push frida-server /data/local/tmp/\nadb shell su -c \"chmod 755 /data/local/tmp/frida-server\"\nadb shell su -c \"/data/local/tmp/frida-server &\"\n\n# Script ausführen\nfrida -U -l crypto_hooks.js com.target.package\n```\n\n## Custom Recovery und Fastboot-Exploits\n\n### TWRP Installation für forensischen Zugang\n```bash\n# Bootloader entsperren (Herstellerabhängig)\nfastboot oem unlock\n# Oder\nfastboot flashing unlock\n\n# TWRP flashen\nfastboot flash recovery twrp-device.img\nfastboot boot twrp-device.img  # Temporäre Installation\n\n# In TWRP: ADB-Zugang mit Root-Berechtigungen\nadb shell mount /system\nadb shell mount /data\n```\n\n### Partitions-Imaging mit dd\n```bash\n# Vollständige Partition-Liste\nadb shell cat /proc/partitions\n\n# Kritische Partitionen extrahieren\nadb shell dd if=/dev/block/bootdevice/by-name/system of=/external_sd/system.img\nadb shell dd if=/dev/block/bootdevice/by-name/userdata of=/external_sd/userdata.img\nadb shell dd if=/dev/block/bootdevice/by-name/boot of=/external_sd/boot.img\n```\n\n## SQLite Forensics und gelöschte Daten\n\n### Erweiterte SQLite-Analyse\n```bash\n# Freelist-Analyse für gelöschte Einträge\nsqlite3 database.db \"PRAGMA freelist_count;\"\nsqlite3 database.db \"PRAGMA page_size;\"\n\n# WAL-Datei Analyse\nsqlite3 database.db \"PRAGMA wal_checkpoint;\"\nstrings database.db-wal | grep -i \"search_term\"\n\n# Undark für Deleted Record Recovery\nundark database.db --freelist --export-csv\n```\n\n### Timeline-Rekonstruktion\n```bash\n# Autopsy Timeline-Generierung\n# Tools → Generate Timeline\n# Analyse von MAC-Times (Modified, Accessed, Created)\n\n# Plaso Timeline-Tools\nlog2timeline.py timeline.plaso /path/to/android/data/\npsort.py -o dynamic timeline.plaso\n```\n\n## Weiterführende Ressourcen\n\n### Dokumentation und Standards\n- [NIST SP 800-101 Rev. 1 - Mobile Device Forensics Guidelines](https://csrc.nist.gov/pubs/sp/800/101/r1/final)\n- [SANS FOR585 - Smartphone Forensics](https://www.sans.org/cyber-security-courses/advanced-smartphone-mobile-device-forensics/)\n- [ALEAPP GitHub Repository](https://github.com/abrignoni/ALEAPP)\n- [MVT Documentation](https://docs.mvt.re/en/latest/)\n\n### Community und Weiterbildung\n- [Autopsy User Documentation](https://sleuthkit.org/autopsy/docs/)\n- [Android Forensics References](https://github.com/impillar/AndroidReferences/blob/master/AndroidTools.md)\n- [Digital Forensics Framework Collection](https://github.com/mesquidar/ForensicsTools)\n\n### Spezialisierte Tools\n- [MTKClient für MediaTek Exploits](https://github.com/bkerler/mtkclient)\n- [Android Forensics Framework](https://github.com/nowsecure/android-forensics)\n- [Santoku Linux Mobile Forensics Distribution](https://santoku-linux.com/)\n\n---\n\n**Wichtiger Hinweis**: Diese Anleitung dient ausschließlich für autorisierte forensische Untersuchungen. Stellen Sie sicher, dass Sie über entsprechende rechtliche Befugnisse verfügen, bevor Sie diese Techniken anwenden. Bei Zweifeln konsultieren Sie Rechtsberatung.","src/content/knowledgebase/android-logical-imaging.md","0bb3f1d2c872d2bf",{"html":307,"metadata":308},"\u003Ch1 id=\"übersicht\">Übersicht\u003C/h1>\n\u003Cp>Open-Source Android Forensik bietet robuste Alternativen zu kommerziellen Lösungen wie Cellebrite UFED und Magnet AXIOM. Besonders für ältere Android-Geräte (5+ Jahre) existieren bewährte Methoden zur Datenextraktion und -analyse.\u003C/p>\n\u003Ch2 id=\"kernkomponenten-des-open-source-forensik-stacks\">Kernkomponenten des Open-Source Forensik-Stacks\u003C/h2>\n\u003Cp>\u003Cstrong>Autopsy Digital Forensics Platform\u003C/strong> bildet das Fundament mit GUI-basierter Analyse und integrierten Android-Parsing-Fähigkeiten. Die Plattform unterstützt \u003Cstrong>ALEAPP (Android Logs Events And Protobuf Parser)\u003C/strong>, das über 100 Artefakt-Kategorien aus Android-Extraktionen parst.\u003C/p>\n\u003Cp>\u003Cstrong>Mobile Verification Toolkit (MVT)\u003C/strong> von Amnesty International bietet spezialisierte Command-Line-Tools für Android-Analyse mit Fokus auf Kompromittierungserkennung.\u003C/p>\n\u003Cp>\u003Cstrong>SIFT Workstation\u003C/strong> stellt eine komplette Ubuntu-basierte forensische Umgebung mit 125+ vorinstallierten Tools bereit.\u003C/p>\n\u003Ch2 id=\"erfolgsraten-nach-gerätealter\">Erfolgsraten nach Gerätealter\u003C/h2>\n\u003Cul>\n\u003Cli>\u003Cstrong>Pre-2017 Geräte\u003C/strong>: 85-98% logische Extraktion, 30-70% physische Extraktion\u003C/li>\n\u003Cli>\u003Cstrong>2017-2019 Geräte\u003C/strong>: 80-95% logische Extraktion, 15-35% physische Extraktion\u003C/li>\n\u003Cli>\u003Cstrong>2020+ Geräte\u003C/strong>: 70-85% logische Extraktion, 5-15% physische Extraktion\u003C/li>\n\u003C/ul>\n\u003Ch1 id=\"installation\">Installation\u003C/h1>\n\u003Ch2 id=\"sift-workstation-setup\">SIFT Workstation Setup\u003C/h2>\n\u003Ch3 id=\"systemanforderungen\">Systemanforderungen\u003C/h3>\n\u003Cul>\n\u003Cli>Quad-Core CPU 2.5GHz+\u003C/li>\n\u003Cli>16GB+ RAM\u003C/li>\n\u003Cli>500GB+ SSD Speicher\u003C/li>\n\u003Cli>USB 3.0+ Anschlüsse\u003C/li>\n\u003C/ul>\n\u003Ch3 id=\"installation-1\">Installation\u003C/h3>\n\u003Col>\n\u003Cli>Download von \u003Ca href=\"https://www.sans.org/tools/sift-workstation/\">SANS SIFT Workstation\u003C/a>\u003C/li>\n\u003Cli>VMware/VirtualBox Import der OVA-Datei\u003C/li>\n\u003Cli>VM-Konfiguration: 8GB+ RAM, 4+ CPU-Kerne\u003C/li>\n\u003C/ol>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"bash\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># Update nach Installation\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">sudo\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> apt\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> update\u003C/span>\u003Cspan style=\"color:#E1E4E8\"> && \u003C/span>\u003Cspan style=\"color:#B392F0\">sudo\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> apt\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> upgrade\u003C/span>\u003Cspan style=\"color:#79B8FF\"> -y\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">sudo\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> sift\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> update\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Ch2 id=\"autopsy-installation\">Autopsy Installation\u003C/h2>\n\u003Ch3 id=\"windows-installation\">Windows Installation\u003C/h3>\n\u003Col>\n\u003Cli>Download von \u003Ca href=\"https://www.autopsy.com/\">autopsy.com\u003C/a>\u003C/li>\n\u003Cli>Java 8+ Installation erforderlich\u003C/li>\n\u003Cli>Installation mit Administratorrechten\u003C/li>\n\u003C/ol>\n\u003Ch3 id=\"linux-installation\">Linux Installation\u003C/h3>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"bash\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># Ubuntu/Debian\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">sudo\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> apt\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> install\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> autopsy\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> sleuthkit\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># Oder manueller Download und Installation\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">wget\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> https://github.com/sleuthkit/autopsy/releases/latest\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Ch2 id=\"essential-tools-installation\">Essential Tools Installation\u003C/h2>\n\u003Ch3 id=\"android-debug-bridge-adb\">Android Debug Bridge (ADB)\u003C/h3>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"bash\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># Ubuntu/Debian\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">sudo\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> apt\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> install\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> android-tools-adb\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> android-tools-fastboot\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># Windows - Download Android Platform Tools\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># https://developer.android.com/studio/releases/platform-tools\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Ch3 id=\"aleapp-installation\">ALEAPP Installation\u003C/h3>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"bash\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">git\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> clone\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> https://github.com/abrignoni/ALEAPP.git\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#79B8FF\">cd\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> ALEAPP\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">pip3\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> install\u003C/span>\u003Cspan style=\"color:#79B8FF\"> -r\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> requirements.txt\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Ch3 id=\"mobile-verification-toolkit-mvt\">Mobile Verification Toolkit (MVT)\u003C/h3>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"bash\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">pip3\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> install\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> mvt\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># Oder via GitHub für neueste Version\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">git\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> clone\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> https://github.com/mvt-project/mvt.git\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#79B8FF\">cd\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> mvt\u003C/span>\u003Cspan style=\"color:#E1E4E8\"> && \u003C/span>\u003Cspan style=\"color:#B392F0\">pip3\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> install\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> .\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Ch3 id=\"andriller-installation\">Andriller Installation\u003C/h3>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"bash\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">git\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> clone\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> https://github.com/den4uk/andriller.git\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#79B8FF\">cd\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> andriller\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">pip3\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> install\u003C/span>\u003Cspan style=\"color:#79B8FF\"> -r\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> requirements.txt\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Ch1 id=\"konfiguration\">Konfiguration\u003C/h1>\n\u003Ch2 id=\"adb-setup-und-gerätevorbereitung\">ADB Setup und Gerätevorbereitung\u003C/h2>\n\u003Ch3 id=\"usb-debugging-aktivieren\">USB-Debugging aktivieren\u003C/h3>\n\u003Col>\n\u003Cli>Entwickleroptionen freischalten (7x Build-Nummer antippen)\u003C/li>\n\u003Cli>USB-Debugging aktivieren\u003C/li>\n\u003Cli>Gerät via USB verbinden\u003C/li>\n\u003Cli>RSA-Fingerprint akzeptieren\u003C/li>\n\u003C/ol>\n\u003Ch3 id=\"adb-verbindung-testen\">ADB Verbindung testen\u003C/h3>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"bash\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">adb\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> devices\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># Sollte Gerät mit \"device\" Status zeigen\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">adb\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> shell\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> getprop\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> ro.build.version.release\u003C/span>\u003Cspan style=\"color:#6A737D\">  # Android Version\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">adb\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> shell\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> getprop\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> ro.product.model\u003C/span>\u003Cspan style=\"color:#6A737D\">         # Gerätemodell\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Ch2 id=\"autopsy-projektkonfiguration\">Autopsy Projektkonfiguration\u003C/h2>\n\u003Ch3 id=\"case-setup\">Case-Setup\u003C/h3>\n\u003Col>\n\u003Cli>Neuen Fall erstellen\u003C/li>\n\u003Cli>Ermittler-Informationen eingeben\u003C/li>\n\u003Cli>Case-Verzeichnis festlegen (ausreichend Speicherplatz)\u003C/li>\n\u003C/ol>\n\u003Ch3 id=\"android-analyzer-module-aktivieren\">Android Analyzer Module aktivieren\u003C/h3>\n\u003Cul>\n\u003Cli>Tools → Options → Modules\u003C/li>\n\u003Cli>Android Analyzer aktivieren\u003C/li>\n\u003Cli>ALEAPP Integration konfigurieren\u003C/li>\n\u003C/ul>\n\u003Ch3 id=\"hash-algorithmen-konfigurieren\">Hash-Algorithmen konfigurieren\u003C/h3>\n\u003Cul>\n\u003Cli>MD5, SHA-1, SHA-256 für Integritätsprüfung\u003C/li>\n\u003Cli>Automatische Hash-Berechnung bei Import aktivieren\u003C/li>\n\u003C/ul>\n\u003Ch2 id=\"mvt-konfiguration\">MVT Konfiguration\u003C/h2>\n\u003Ch3 id=\"konfigurationsdatei-erstellen\">Konfigurationsdatei erstellen\u003C/h3>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"yaml\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># ~/.mvt/config.yaml\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#85E89D\">adb_path\u003C/span>\u003Cspan style=\"color:#E1E4E8\">: \u003C/span>\u003Cspan style=\"color:#9ECBFF\">\"/usr/bin/adb\"\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#85E89D\">output_folder\u003C/span>\u003Cspan style=\"color:#E1E4E8\">: \u003C/span>\u003Cspan style=\"color:#9ECBFF\">\"/home/user/mvt_output\"\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Ch1 id=\"verwendungsbeispiele\">Verwendungsbeispiele\u003C/h1>\n\u003Ch2 id=\"fall-1-logische-datenextraktion-mit-adb\">Fall 1: Logische Datenextraktion mit ADB\u003C/h2>\n\u003Ch3 id=\"geräteinformationen-sammeln\">Geräteinformationen sammeln\u003C/h3>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"bash\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># Systeminfo\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">adb\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> shell\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> getprop\u003C/span>\u003Cspan style=\"color:#F97583\"> >\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> device_properties.txt\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">adb\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> shell\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> cat\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> /proc/version\u003C/span>\u003Cspan style=\"color:#F97583\"> >\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> kernel_info.txt\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">adb\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> shell\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> mount\u003C/span>\u003Cspan style=\"color:#F97583\"> >\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> mount_info.txt\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># Installierte Apps\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">adb\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> shell\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> pm\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> list\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> packages\u003C/span>\u003Cspan style=\"color:#79B8FF\"> -f\u003C/span>\u003Cspan style=\"color:#F97583\"> >\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> installed_packages.txt\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Ch3 id=\"datenbank-extraktion\">Datenbank-Extraktion\u003C/h3>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"bash\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># SMS/MMS Datenbank\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">adb\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> pull\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> /data/data/com.android.providers.telephony/databases/mmssms.db\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># Kontakte\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">adb\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> pull\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> /data/data/com.android.providers.contacts/databases/contacts2.db\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># Anrufliste  \u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">adb\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> pull\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> /data/data/com.android.providers.contacts/databases/calllog.db\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Ch3 id=\"whatsapp-datenextraktion\">WhatsApp Datenextraktion\u003C/h3>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"bash\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># WhatsApp Datenbanken (Root erforderlich)\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">adb\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> shell\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> su\u003C/span>\u003Cspan style=\"color:#79B8FF\"> -c\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> \"cp -r /data/data/com.whatsapp/ /sdcard/whatsapp_backup/\"\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">adb\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> pull\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> /sdcard/whatsapp_backup/\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Ch2 id=\"fall-2-android-backup-analyse\">Fall 2: Android Backup-Analyse\u003C/h2>\n\u003Ch3 id=\"vollständiges-backup-erstellen\">Vollständiges Backup erstellen\u003C/h3>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"bash\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># Umfassendes Backup (ohne Root)\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">adb\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> backup\u003C/span>\u003Cspan style=\"color:#79B8FF\"> -all\u003C/span>\u003Cspan style=\"color:#79B8FF\"> -system\u003C/span>\u003Cspan style=\"color:#79B8FF\"> -apk\u003C/span>\u003Cspan style=\"color:#79B8FF\"> -shared\u003C/span>\u003Cspan style=\"color:#79B8FF\"> -f\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> backup.ab\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># Backup entschlüsseln (falls verschlüsselt)\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">java\u003C/span>\u003Cspan style=\"color:#79B8FF\"> -jar\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> abe.jar\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> unpack\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> backup.ab\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> backup.tar\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">tar\u003C/span>\u003Cspan style=\"color:#79B8FF\"> -xf\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> backup.tar\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Ch3 id=\"backup-mit-aleapp-analysieren\">Backup mit ALEAPP analysieren\u003C/h3>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"bash\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">python3\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> aleappGUI.py\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># Oder Command-Line\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">python3\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> aleapp.py\u003C/span>\u003Cspan style=\"color:#79B8FF\"> -t\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> tar\u003C/span>\u003Cspan style=\"color:#79B8FF\"> -i\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> backup.tar\u003C/span>\u003Cspan style=\"color:#79B8FF\"> -o\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> output_folder\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Ch2 id=\"fall-3-mvt-kompromittierungsanalyse\">Fall 3: MVT Kompromittierungsanalyse\u003C/h2>\n\u003Ch3 id=\"live-geräteanalyse\">Live-Geräteanalyse\u003C/h3>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"bash\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># ADB-basierte Analyse\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">mvt-android\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> check-adb\u003C/span>\u003Cspan style=\"color:#79B8FF\"> --output\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> /path/to/output/\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># Backup-Analyse\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">mvt-android\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> check-backup\u003C/span>\u003Cspan style=\"color:#79B8FF\"> --output\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> /path/to/output/\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> backup.ab\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Ch3 id=\"ioc-suche-mit-pegasus-indikatoren\">IOC-Suche mit Pegasus-Indikatoren\u003C/h3>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"bash\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># Mit vorgefertigten IOCs\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">mvt-android\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> check-adb\u003C/span>\u003Cspan style=\"color:#79B8FF\"> --iocs\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> /path/to/pegasus.stix2\u003C/span>\u003Cspan style=\"color:#79B8FF\"> --output\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> results/\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Ch2 id=\"fall-4-physische-extraktion-root-erforderlich\">Fall 4: Physische Extraktion (Root erforderlich)\u003C/h2>\n\u003Ch3 id=\"device-rooting---mediatek-geräte\">Device Rooting - MediaTek Geräte\u003C/h3>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"bash\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># MTKClient für MediaTek-Chipsets\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">git\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> clone\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> https://github.com/bkerler/mtkclient.git\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#79B8FF\">cd\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> mtkclient\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">python3\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> mtk\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> payload\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># Nach erfolgreichem Root\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">adb\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> shell\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> su\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Ch3 id=\"vollständiges-memory-dump\">Vollständiges Memory Dump\u003C/h3>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"bash\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># Partitionslayout ermitteln\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">adb\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> shell\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> su\u003C/span>\u003Cspan style=\"color:#79B8FF\"> -c\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> \"cat /proc/partitions\"\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">adb\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> shell\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> su\u003C/span>\u003Cspan style=\"color:#79B8FF\"> -c\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> \"ls -la /dev/block/\"\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># Vollständiges Device Image (Root erforderlich)\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">adb\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> shell\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> su\u003C/span>\u003Cspan style=\"color:#79B8FF\"> -c\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> \"dd if=/dev/block/mmcblk0 of=/sdcard/full_device.img bs=4096\"\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">adb\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> pull\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> /sdcard/full_device.img\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Ch1 id=\"best-practices\">Best Practices\u003C/h1>\n\u003Ch2 id=\"rechtliche-compliance\">Rechtliche Compliance\u003C/h2>\n\u003Ch3 id=\"dokumentation-und-chain-of-custody\">Dokumentation und Chain of Custody\u003C/h3>\n\u003Cul>\n\u003Cli>\u003Cstrong>Vollständige Dokumentation\u003C/strong>: Wer, Was, Wann, Wo, Warum\u003C/li>\n\u003Cli>\u003Cstrong>Hash-Verifikation\u003C/strong>: MD5/SHA-256 für alle extrahierten Daten\u003C/li>\n\u003Cli>\u003Cstrong>Nur forensische Kopien analysieren\u003C/strong>, niemals Originaldaten\u003C/li>\n\u003Cli>\u003Cstrong>Schriftliche Genehmigung\u003C/strong> für Geräteanalyse einholen\u003C/li>\n\u003C/ul>\n\u003Ch3 id=\"familiengeräte-und-nachlässe\">Familiengeräte und Nachlässe\u003C/h3>\n\u003Cul>\n\u003Cli>Genehmigung durch Nachlassverwalter erforderlich\u003C/li>\n\u003Cli>Gerichtsbeschlüsse für Cloud-Zugang eventuell nötig\u003C/li>\n\u003Cli>Drittpartei-Kommunikation kann weiterhin geschützt sein\u003C/li>\n\u003C/ul>\n\u003Ch2 id=\"technische-best-practices\">Technische Best Practices\u003C/h2>\n\u003Ch3 id=\"hash-integrität-sicherstellen\">Hash-Integrität sicherstellen\u003C/h3>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"bash\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># Hash vor und nach Transfer prüfen\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">md5sum\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> original_file.db\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">sha256sum\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> original_file.db\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># Hash-Verifikation dokumentieren\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#79B8FF\">echo\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> \"$(\u003C/span>\u003Cspan style=\"color:#B392F0\">date\u003C/span>\u003Cspan style=\"color:#9ECBFF\">): MD5: $(\u003C/span>\u003Cspan style=\"color:#B392F0\">md5sum\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> file.db)\"\u003C/span>\u003Cspan style=\"color:#F97583\"> >>\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> chain_of_custody.log\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Ch3 id=\"sichere-arbeitsumgebung\">Sichere Arbeitsumgebung\u003C/h3>\n\u003Cul>\n\u003Cli>Isolierte VM für Forensik-Arbeit\u003C/li>\n\u003Cli>Netzwerk-Isolation während Analyse\u003C/li>\n\u003Cli>Verschlüsselte Speicherung aller Evidenz\u003C/li>\n\u003Cli>Regelmäßige Backups der Case-Datenbanken\u003C/li>\n\u003C/ul>\n\u003Ch3 id=\"qualitätssicherung\">Qualitätssicherung\u003C/h3>\n\u003Cul>\n\u003Cli>Peer-Review kritischer Analysen\u003C/li>\n\u003Cli>Standardisierte Arbeitsabläufe (SOPs)\u003C/li>\n\u003Cli>Regelmäßige Tool-Validierung\u003C/li>\n\u003Cli>Kontinuierliche Weiterbildung\u003C/li>\n\u003C/ul>\n\u003Ch2 id=\"erfolgsmaximierung-nach-gerätehersteller\">Erfolgsmaximierung nach Gerätehersteller\u003C/h2>\n\u003Ch3 id=\"mediatek-geräte-höchste-erfolgsrate\">MediaTek-Geräte (Höchste Erfolgsrate)\u003C/h3>\n\u003Cul>\n\u003Cli>BootROM-Exploits für MT6735, MT6737, MT6750, MT6753, MT6797\u003C/li>\n\u003Cli>MTKClient für Hardware-Level-Zugang\u003C/li>\n\u003Cli>Erfolgsrate: 80%+ für Geräte 2015-2019\u003C/li>\n\u003C/ul>\n\u003Ch3 id=\"samsung-geräte\">Samsung-Geräte\u003C/h3>\n\u003Cul>\n\u003Cli>Ältere Knox-Implementierungen umgehbar\u003C/li>\n\u003Cli>Emergency Dialer Exploits für Android 4.x\u003C/li>\n\u003Cli>Erfolgsrate: 40-70% je nach Knox-Version\u003C/li>\n\u003C/ul>\n\u003Ch3 id=\"pixelnexus-geräte\">Pixel/Nexus-Geräte\u003C/h3>\n\u003Cul>\n\u003Cli>Bootloader-Unlocking oft möglich\u003C/li>\n\u003Cli>Fastboot-basierte Recovery-Installation\u003C/li>\n\u003Cli>Erfolgsrate: 60-80% bei freigeschaltetem Bootloader\u003C/li>\n\u003C/ul>\n\u003Ch1 id=\"troubleshooting\">Troubleshooting\u003C/h1>\n\u003Ch2 id=\"problem-adb-erkennt-gerät-nicht\">Problem: ADB erkennt Gerät nicht\u003C/h2>\n\u003Ch3 id=\"lösung-usb-treiber-und-berechtigungen\">Lösung: USB-Treiber und Berechtigungen\u003C/h3>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"bash\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># Linux: USB-Berechtigungen prüfen\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">lsusb\u003C/span>\u003Cspan style=\"color:#F97583\"> |\u003C/span>\u003Cspan style=\"color:#B392F0\"> grep\u003C/span>\u003Cspan style=\"color:#79B8FF\"> -i\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> android\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">sudo\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> chmod\u003C/span>\u003Cspan style=\"color:#79B8FF\"> 666\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> /dev/bus/usb/XXX/XXX\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># udev-Regeln erstellen\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#79B8FF\">echo\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> 'SUBSYSTEM==\"usb\", ATTR{idVendor}==\"18d1\", MODE=\"0666\", GROUP=\"plugdev\"'\u003C/span>\u003Cspan style=\"color:#F97583\"> |\u003C/span>\u003Cspan style=\"color:#B392F0\"> sudo\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> tee\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> /etc/udev/rules.d/51-android.rules\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">sudo\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> udevadm\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> control\u003C/span>\u003Cspan style=\"color:#79B8FF\"> --reload-rules\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Ch3 id=\"windows-treiber-installation\">Windows: Treiber-Installation\u003C/h3>\n\u003Col>\n\u003Cli>Geräte-Manager öffnen\u003C/li>\n\u003Cli>Android-Gerät mit Warnsymbol finden\u003C/li>\n\u003Cli>Treiber manuell installieren (Android USB Driver)\u003C/li>\n\u003C/ol>\n\u003Ch2 id=\"problem-verschlüsselte-android-backups\">Problem: Verschlüsselte Android Backups\u003C/h2>\n\u003Ch3 id=\"lösung-android-backup-extractor\">Lösung: Android Backup Extractor\u003C/h3>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"bash\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># ADB Backup Extractor installieren\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">git\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> clone\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> https://github.com/nelenkov/android-backup-extractor.git\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#79B8FF\">cd\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> android-backup-extractor\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">gradle\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> build\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># Backup entschlüsseln\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">java\u003C/span>\u003Cspan style=\"color:#79B8FF\"> -jar\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> abe.jar\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> unpack\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> backup.ab\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> backup.tar\u003C/span>\u003Cspan style=\"color:#E1E4E8\"> [password]\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Ch2 id=\"problem-unzureichende-berechtigungen-für-datenextraktion\">Problem: Unzureichende Berechtigungen für Datenextraktion\u003C/h2>\n\u003Ch3 id=\"lösung-alternative-extraktionsmethoden\">Lösung: Alternative Extraktionsmethoden\u003C/h3>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"bash\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># AFLogical OSE für begrenzte Extraktion ohne Root\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># WhatsApp Key/DB Extractor für spezifische Apps\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># Backup-basierte Extraktion als Fallback\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># Custom Recovery für erweiterten Zugang\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">fastboot\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> flash\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> recovery\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> twrp-device.img\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Ch2 id=\"problem-aleapp-parsing-fehler\">Problem: ALEAPP Parsing-Fehler\u003C/h2>\n\u003Ch3 id=\"lösung-datenformat-probleme-beheben\">Lösung: Datenformat-Probleme beheben\u003C/h3>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"bash\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># Log-Dateien prüfen\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">python3\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> aleapp.py\u003C/span>\u003Cspan style=\"color:#79B8FF\"> -t\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> dir\u003C/span>\u003Cspan style=\"color:#79B8FF\"> -i\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> /path/to/data\u003C/span>\u003Cspan style=\"color:#79B8FF\"> -o\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> output\u003C/span>\u003Cspan style=\"color:#79B8FF\"> --debug\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># Spezifische Parser deaktivieren\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># Manuelle SQLite-Analyse bei Parser-Fehlern\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">sqlite3\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> database.db\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> \".tables\"\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">sqlite3\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> database.db\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> \".schema table_name\"\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Ch1 id=\"erweiterte-techniken\">Erweiterte Techniken\u003C/h1>\n\u003Ch2 id=\"memory-forensics-mit-lime\">Memory Forensics mit LiME\u003C/h2>\n\u003Ch3 id=\"lime-für-arm-devices-kompilieren\">LiME für ARM-Devices kompilieren\u003C/h3>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"bash\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># Cross-Compilation Setup\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#F97583\">export\u003C/span>\u003Cspan style=\"color:#E1E4E8\"> ARCH\u003C/span>\u003Cspan style=\"color:#F97583\">=\u003C/span>\u003Cspan style=\"color:#E1E4E8\">arm\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#F97583\">export\u003C/span>\u003Cspan style=\"color:#E1E4E8\"> CROSS_COMPILE\u003C/span>\u003Cspan style=\"color:#F97583\">=\u003C/span>\u003Cspan style=\"color:#E1E4E8\">arm-linux-gnueabi-\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#F97583\">export\u003C/span>\u003Cspan style=\"color:#E1E4E8\"> KERNEL_DIR\u003C/span>\u003Cspan style=\"color:#F97583\">=\u003C/span>\u003Cspan style=\"color:#E1E4E8\">/path/to/kernel/source\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># LiME Module kompilieren\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">git\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> clone\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> https://github.com/504ensicsLabs/LiME.git\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#79B8FF\">cd\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> LiME/src\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">make\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># Memory Dump erstellen (Root erforderlich)\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">adb\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> push\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> lime.ko\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> /data/local/tmp/\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">adb\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> shell\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> su\u003C/span>\u003Cspan style=\"color:#79B8FF\"> -c\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> \"insmod /data/local/tmp/lime.ko 'path=/sdcard/memory.lime format=lime'\"\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Ch3 id=\"volatility-analyse-von-android-memory\">Volatility-Analyse von Android Memory\u003C/h3>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"bash\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># Memory Dump analysieren\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">python\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> vol.py\u003C/span>\u003Cspan style=\"color:#79B8FF\"> -f\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> memory.lime\u003C/span>\u003Cspan style=\"color:#79B8FF\"> --profile=Linux\u003C/span>\u003Cspan style=\"color:#F97583\"> <\u003C/span>\u003Cspan style=\"color:#9ECBFF\">profil\u003C/span>\u003Cspan style=\"color:#E1E4E8\">e\u003C/span>\u003Cspan style=\"color:#F97583\">>\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> linux.pslist\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">python\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> vol.py\u003C/span>\u003Cspan style=\"color:#79B8FF\"> -f\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> memory.lime\u003C/span>\u003Cspan style=\"color:#79B8FF\"> --profile=Linux\u003C/span>\u003Cspan style=\"color:#F97583\"> <\u003C/span>\u003Cspan style=\"color:#9ECBFF\">profil\u003C/span>\u003Cspan style=\"color:#E1E4E8\">e\u003C/span>\u003Cspan style=\"color:#F97583\">>\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> linux.bash\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">python\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> vol.py\u003C/span>\u003Cspan style=\"color:#79B8FF\"> -f\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> memory.lime\u003C/span>\u003Cspan style=\"color:#79B8FF\"> --profile=Linux\u003C/span>\u003Cspan style=\"color:#F97583\"> <\u003C/span>\u003Cspan style=\"color:#9ECBFF\">profil\u003C/span>\u003Cspan style=\"color:#E1E4E8\">e\u003C/span>\u003Cspan style=\"color:#F97583\">>\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> linux.netstat\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Ch2 id=\"frida-basierte-runtime-analyse\">FRIDA-basierte Runtime-Analyse\u003C/h2>\n\u003Ch3 id=\"frida-für-kryptographie-hooks\">FRIDA für Kryptographie-Hooks\u003C/h3>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"javascript\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\">// crypto_hooks.js - SSL/TLS Traffic abfangen\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#E1E4E8\">Java.\u003C/span>\u003Cspan style=\"color:#B392F0\">perform\u003C/span>\u003Cspan style=\"color:#E1E4E8\">(\u003C/span>\u003Cspan style=\"color:#F97583\">function\u003C/span>\u003Cspan style=\"color:#E1E4E8\">() {\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#F97583\">    var\u003C/span>\u003Cspan style=\"color:#E1E4E8\"> SSLContext \u003C/span>\u003Cspan style=\"color:#F97583\">=\u003C/span>\u003Cspan style=\"color:#E1E4E8\"> Java.\u003C/span>\u003Cspan style=\"color:#B392F0\">use\u003C/span>\u003Cspan style=\"color:#E1E4E8\">(\u003C/span>\u003Cspan style=\"color:#9ECBFF\">\"javax.net.ssl.SSLContext\"\u003C/span>\u003Cspan style=\"color:#E1E4E8\">);\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#E1E4E8\">    SSLContext.init.\u003C/span>\u003Cspan style=\"color:#B392F0\">overload\u003C/span>\u003Cspan style=\"color:#E1E4E8\">(\u003C/span>\u003Cspan style=\"color:#9ECBFF\">'[Ljavax.net.ssl.KeyManager;'\u003C/span>\u003Cspan style=\"color:#E1E4E8\">, \u003C/span>\u003Cspan style=\"color:#9ECBFF\">'[Ljavax.net.ssl.TrustManager;'\u003C/span>\u003Cspan style=\"color:#E1E4E8\">, \u003C/span>\u003Cspan style=\"color:#9ECBFF\">'java.security.SecureRandom'\u003C/span>\u003Cspan style=\"color:#E1E4E8\">).\u003C/span>\u003Cspan style=\"color:#B392F0\">implementation\u003C/span>\u003Cspan style=\"color:#F97583\"> =\u003C/span>\u003Cspan style=\"color:#F97583\"> function\u003C/span>\u003Cspan style=\"color:#E1E4E8\">(\u003C/span>\u003Cspan style=\"color:#FFAB70\">keyManagers\u003C/span>\u003Cspan style=\"color:#E1E4E8\">, \u003C/span>\u003Cspan style=\"color:#FFAB70\">trustManagers\u003C/span>\u003Cspan style=\"color:#E1E4E8\">, \u003C/span>\u003Cspan style=\"color:#FFAB70\">secureRandom\u003C/span>\u003Cspan style=\"color:#E1E4E8\">) {\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#E1E4E8\">        console.\u003C/span>\u003Cspan style=\"color:#B392F0\">log\u003C/span>\u003Cspan style=\"color:#E1E4E8\">(\u003C/span>\u003Cspan style=\"color:#9ECBFF\">\"[+] SSLContext.init() called\"\u003C/span>\u003Cspan style=\"color:#E1E4E8\">);\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#79B8FF\">        this\u003C/span>\u003Cspan style=\"color:#E1E4E8\">.\u003C/span>\u003Cspan style=\"color:#B392F0\">init\u003C/span>\u003Cspan style=\"color:#E1E4E8\">(keyManagers, trustManagers, secureRandom);\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#E1E4E8\">    };\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#E1E4E8\">});\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Ch3 id=\"frida-installation-und-verwendung\">FRIDA Installation und Verwendung\u003C/h3>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"bash\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># FRIDA Server auf Android-Gerät installieren\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">adb\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> push\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> frida-server\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> /data/local/tmp/\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">adb\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> shell\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> su\u003C/span>\u003Cspan style=\"color:#79B8FF\"> -c\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> \"chmod 755 /data/local/tmp/frida-server\"\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">adb\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> shell\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> su\u003C/span>\u003Cspan style=\"color:#79B8FF\"> -c\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> \"/data/local/tmp/frida-server &\"\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># Script ausführen\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">frida\u003C/span>\u003Cspan style=\"color:#79B8FF\"> -U\u003C/span>\u003Cspan style=\"color:#79B8FF\"> -l\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> crypto_hooks.js\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> com.target.package\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Ch2 id=\"custom-recovery-und-fastboot-exploits\">Custom Recovery und Fastboot-Exploits\u003C/h2>\n\u003Ch3 id=\"twrp-installation-für-forensischen-zugang\">TWRP Installation für forensischen Zugang\u003C/h3>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"bash\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># Bootloader entsperren (Herstellerabhängig)\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">fastboot\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> oem\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> unlock\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># Oder\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">fastboot\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> flashing\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> unlock\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># TWRP flashen\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">fastboot\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> flash\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> recovery\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> twrp-device.img\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">fastboot\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> boot\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> twrp-device.img\u003C/span>\u003Cspan style=\"color:#6A737D\">  # Temporäre Installation\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># In TWRP: ADB-Zugang mit Root-Berechtigungen\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">adb\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> shell\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> mount\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> /system\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">adb\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> shell\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> mount\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> /data\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Ch3 id=\"partitions-imaging-mit-dd\">Partitions-Imaging mit dd\u003C/h3>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"bash\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># Vollständige Partition-Liste\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">adb\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> shell\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> cat\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> /proc/partitions\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># Kritische Partitionen extrahieren\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">adb\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> shell\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> dd\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> if=/dev/block/bootdevice/by-name/system\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> of=/external_sd/system.img\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">adb\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> shell\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> dd\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> if=/dev/block/bootdevice/by-name/userdata\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> of=/external_sd/userdata.img\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">adb\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> shell\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> dd\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> if=/dev/block/bootdevice/by-name/boot\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> of=/external_sd/boot.img\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Ch2 id=\"sqlite-forensics-und-gelöschte-daten\">SQLite Forensics und gelöschte Daten\u003C/h2>\n\u003Ch3 id=\"erweiterte-sqlite-analyse\">Erweiterte SQLite-Analyse\u003C/h3>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"bash\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># Freelist-Analyse für gelöschte Einträge\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">sqlite3\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> database.db\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> \"PRAGMA freelist_count;\"\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">sqlite3\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> database.db\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> \"PRAGMA page_size;\"\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># WAL-Datei Analyse\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">sqlite3\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> database.db\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> \"PRAGMA wal_checkpoint;\"\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">strings\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> database.db-wal\u003C/span>\u003Cspan style=\"color:#F97583\"> |\u003C/span>\u003Cspan style=\"color:#B392F0\"> grep\u003C/span>\u003Cspan style=\"color:#79B8FF\"> -i\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> \"search_term\"\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># Undark für Deleted Record Recovery\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">undark\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> database.db\u003C/span>\u003Cspan style=\"color:#79B8FF\"> --freelist\u003C/span>\u003Cspan style=\"color:#79B8FF\"> --export-csv\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Ch3 id=\"timeline-rekonstruktion\">Timeline-Rekonstruktion\u003C/h3>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"bash\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># Autopsy Timeline-Generierung\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># Tools → Generate Timeline\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># Analyse von MAC-Times (Modified, Accessed, Created)\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#6A737D\"># Plaso Timeline-Tools\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">log2timeline.py\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> timeline.plaso\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> /path/to/android/data/\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">psort.py\u003C/span>\u003Cspan style=\"color:#79B8FF\"> -o\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> dynamic\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> timeline.plaso\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Ch2 id=\"weiterführende-ressourcen\">Weiterführende Ressourcen\u003C/h2>\n\u003Ch3 id=\"dokumentation-und-standards\">Dokumentation und Standards\u003C/h3>\n\u003Cul>\n\u003Cli>\u003Ca href=\"https://csrc.nist.gov/pubs/sp/800/101/r1/final\">NIST SP 800-101 Rev. 1 - Mobile Device Forensics Guidelines\u003C/a>\u003C/li>\n\u003Cli>\u003Ca href=\"https://www.sans.org/cyber-security-courses/advanced-smartphone-mobile-device-forensics/\">SANS FOR585 - Smartphone Forensics\u003C/a>\u003C/li>\n\u003Cli>\u003Ca href=\"https://github.com/abrignoni/ALEAPP\">ALEAPP GitHub Repository\u003C/a>\u003C/li>\n\u003Cli>\u003Ca href=\"https://docs.mvt.re/en/latest/\">MVT Documentation\u003C/a>\u003C/li>\n\u003C/ul>\n\u003Ch3 id=\"community-und-weiterbildung\">Community und Weiterbildung\u003C/h3>\n\u003Cul>\n\u003Cli>\u003Ca href=\"https://sleuthkit.org/autopsy/docs/\">Autopsy User Documentation\u003C/a>\u003C/li>\n\u003Cli>\u003Ca href=\"https://github.com/impillar/AndroidReferences/blob/master/AndroidTools.md\">Android Forensics References\u003C/a>\u003C/li>\n\u003Cli>\u003Ca href=\"https://github.com/mesquidar/ForensicsTools\">Digital Forensics Framework Collection\u003C/a>\u003C/li>\n\u003C/ul>\n\u003Ch3 id=\"spezialisierte-tools\">Spezialisierte Tools\u003C/h3>\n\u003Cul>\n\u003Cli>\u003Ca href=\"https://github.com/bkerler/mtkclient\">MTKClient für MediaTek Exploits\u003C/a>\u003C/li>\n\u003Cli>\u003Ca href=\"https://github.com/nowsecure/android-forensics\">Android Forensics Framework\u003C/a>\u003C/li>\n\u003Cli>\u003Ca href=\"https://santoku-linux.com/\">Santoku Linux Mobile Forensics Distribution\u003C/a>\u003C/li>\n\u003C/ul>\n\u003Chr>\n\u003Cp>\u003Cstrong>Wichtiger Hinweis\u003C/strong>: Diese Anleitung dient ausschließlich für autorisierte forensische Untersuchungen. Stellen Sie sicher, dass Sie über entsprechende rechtliche Befugnisse verfügen, bevor Sie diese Techniken anwenden. Bei Zweifeln konsultieren Sie Rechtsberatung.\u003C/p>",{"headings":309,"localImagePaths":531,"remoteImagePaths":532,"frontmatter":290,"imagePaths":533},[310,311,314,317,318,321,324,326,329,332,335,338,341,344,347,350,351,354,357,360,363,366,369,372,375,378,379,382,385,388,391,394,397,400,403,406,409,412,415,418,419,422,425,428,431,434,437,440,443,446,449,452,453,456,459,462,465,468,471,474,477,480,483,486,489,492,495,498,501,504,507,510,513,516,519,522,525,528],{"depth":41,"slug":42,"text":43},{"depth":45,"slug":312,"text":313},"kernkomponenten-des-open-source-forensik-stacks","Kernkomponenten des Open-Source Forensik-Stacks",{"depth":45,"slug":315,"text":316},"erfolgsraten-nach-gerätealter","Erfolgsraten nach Gerätealter",{"depth":41,"slug":104,"text":105},{"depth":45,"slug":319,"text":320},"sift-workstation-setup","SIFT Workstation Setup",{"depth":49,"slug":322,"text":323},"systemanforderungen","Systemanforderungen",{"depth":49,"slug":325,"text":105},"installation-1",{"depth":45,"slug":327,"text":328},"autopsy-installation","Autopsy Installation",{"depth":49,"slug":330,"text":331},"windows-installation","Windows Installation",{"depth":49,"slug":333,"text":334},"linux-installation","Linux Installation",{"depth":45,"slug":336,"text":337},"essential-tools-installation","Essential Tools Installation",{"depth":49,"slug":339,"text":340},"android-debug-bridge-adb","Android Debug Bridge (ADB)",{"depth":49,"slug":342,"text":343},"aleapp-installation","ALEAPP Installation",{"depth":49,"slug":345,"text":346},"mobile-verification-toolkit-mvt","Mobile Verification Toolkit (MVT)",{"depth":49,"slug":348,"text":349},"andriller-installation","Andriller Installation",{"depth":41,"slug":113,"text":114},{"depth":45,"slug":352,"text":353},"adb-setup-und-gerätevorbereitung","ADB Setup und Gerätevorbereitung",{"depth":49,"slug":355,"text":356},"usb-debugging-aktivieren","USB-Debugging aktivieren",{"depth":49,"slug":358,"text":359},"adb-verbindung-testen","ADB Verbindung testen",{"depth":45,"slug":361,"text":362},"autopsy-projektkonfiguration","Autopsy Projektkonfiguration",{"depth":49,"slug":364,"text":365},"case-setup","Case-Setup",{"depth":49,"slug":367,"text":368},"android-analyzer-module-aktivieren","Android Analyzer Module aktivieren",{"depth":49,"slug":370,"text":371},"hash-algorithmen-konfigurieren","Hash-Algorithmen konfigurieren",{"depth":45,"slug":373,"text":374},"mvt-konfiguration","MVT Konfiguration",{"depth":49,"slug":376,"text":377},"konfigurationsdatei-erstellen","Konfigurationsdatei erstellen",{"depth":41,"slug":46,"text":47},{"depth":45,"slug":380,"text":381},"fall-1-logische-datenextraktion-mit-adb","Fall 1: Logische Datenextraktion mit ADB",{"depth":49,"slug":383,"text":384},"geräteinformationen-sammeln","Geräteinformationen sammeln",{"depth":49,"slug":386,"text":387},"datenbank-extraktion","Datenbank-Extraktion",{"depth":49,"slug":389,"text":390},"whatsapp-datenextraktion","WhatsApp Datenextraktion",{"depth":45,"slug":392,"text":393},"fall-2-android-backup-analyse","Fall 2: Android Backup-Analyse",{"depth":49,"slug":395,"text":396},"vollständiges-backup-erstellen","Vollständiges Backup erstellen",{"depth":49,"slug":398,"text":399},"backup-mit-aleapp-analysieren","Backup mit ALEAPP analysieren",{"depth":45,"slug":401,"text":402},"fall-3-mvt-kompromittierungsanalyse","Fall 3: MVT Kompromittierungsanalyse",{"depth":49,"slug":404,"text":405},"live-geräteanalyse","Live-Geräteanalyse",{"depth":49,"slug":407,"text":408},"ioc-suche-mit-pegasus-indikatoren","IOC-Suche mit Pegasus-Indikatoren",{"depth":45,"slug":410,"text":411},"fall-4-physische-extraktion-root-erforderlich","Fall 4: Physische Extraktion (Root erforderlich)",{"depth":49,"slug":413,"text":414},"device-rooting---mediatek-geräte","Device Rooting - MediaTek Geräte",{"depth":49,"slug":416,"text":417},"vollständiges-memory-dump","Vollständiges Memory Dump",{"depth":41,"slug":62,"text":63},{"depth":45,"slug":420,"text":421},"rechtliche-compliance","Rechtliche Compliance",{"depth":49,"slug":423,"text":424},"dokumentation-und-chain-of-custody","Dokumentation und Chain of Custody",{"depth":49,"slug":426,"text":427},"familiengeräte-und-nachlässe","Familiengeräte und Nachlässe",{"depth":45,"slug":429,"text":430},"technische-best-practices","Technische Best Practices",{"depth":49,"slug":432,"text":433},"hash-integrität-sicherstellen","Hash-Integrität sicherstellen",{"depth":49,"slug":435,"text":436},"sichere-arbeitsumgebung","Sichere Arbeitsumgebung",{"depth":49,"slug":438,"text":439},"qualitätssicherung","Qualitätssicherung",{"depth":45,"slug":441,"text":442},"erfolgsmaximierung-nach-gerätehersteller","Erfolgsmaximierung nach Gerätehersteller",{"depth":49,"slug":444,"text":445},"mediatek-geräte-höchste-erfolgsrate","MediaTek-Geräte (Höchste Erfolgsrate)",{"depth":49,"slug":447,"text":448},"samsung-geräte","Samsung-Geräte",{"depth":49,"slug":450,"text":451},"pixelnexus-geräte","Pixel/Nexus-Geräte",{"depth":41,"slug":133,"text":134},{"depth":45,"slug":454,"text":455},"problem-adb-erkennt-gerät-nicht","Problem: ADB erkennt Gerät nicht",{"depth":49,"slug":457,"text":458},"lösung-usb-treiber-und-berechtigungen","Lösung: USB-Treiber und Berechtigungen",{"depth":49,"slug":460,"text":461},"windows-treiber-installation","Windows: Treiber-Installation",{"depth":45,"slug":463,"text":464},"problem-verschlüsselte-android-backups","Problem: Verschlüsselte Android Backups",{"depth":49,"slug":466,"text":467},"lösung-android-backup-extractor","Lösung: Android Backup Extractor",{"depth":45,"slug":469,"text":470},"problem-unzureichende-berechtigungen-für-datenextraktion","Problem: Unzureichende Berechtigungen für Datenextraktion",{"depth":49,"slug":472,"text":473},"lösung-alternative-extraktionsmethoden","Lösung: Alternative Extraktionsmethoden",{"depth":45,"slug":475,"text":476},"problem-aleapp-parsing-fehler","Problem: ALEAPP Parsing-Fehler",{"depth":49,"slug":478,"text":479},"lösung-datenformat-probleme-beheben","Lösung: Datenformat-Probleme beheben",{"depth":41,"slug":481,"text":482},"erweiterte-techniken","Erweiterte Techniken",{"depth":45,"slug":484,"text":485},"memory-forensics-mit-lime","Memory Forensics mit LiME",{"depth":49,"slug":487,"text":488},"lime-für-arm-devices-kompilieren","LiME für ARM-Devices kompilieren",{"depth":49,"slug":490,"text":491},"volatility-analyse-von-android-memory","Volatility-Analyse von Android Memory",{"depth":45,"slug":493,"text":494},"frida-basierte-runtime-analyse","FRIDA-basierte Runtime-Analyse",{"depth":49,"slug":496,"text":497},"frida-für-kryptographie-hooks","FRIDA für Kryptographie-Hooks",{"depth":49,"slug":499,"text":500},"frida-installation-und-verwendung","FRIDA Installation und Verwendung",{"depth":45,"slug":502,"text":503},"custom-recovery-und-fastboot-exploits","Custom Recovery und Fastboot-Exploits",{"depth":49,"slug":505,"text":506},"twrp-installation-für-forensischen-zugang","TWRP Installation für forensischen Zugang",{"depth":49,"slug":508,"text":509},"partitions-imaging-mit-dd","Partitions-Imaging mit dd",{"depth":45,"slug":511,"text":512},"sqlite-forensics-und-gelöschte-daten","SQLite Forensics und gelöschte Daten",{"depth":49,"slug":514,"text":515},"erweiterte-sqlite-analyse","Erweiterte SQLite-Analyse",{"depth":49,"slug":517,"text":518},"timeline-rekonstruktion","Timeline-Rekonstruktion",{"depth":45,"slug":520,"text":521},"weiterführende-ressourcen","Weiterführende Ressourcen",{"depth":49,"slug":523,"text":524},"dokumentation-und-standards","Dokumentation und Standards",{"depth":49,"slug":526,"text":527},"community-und-weiterbildung","Community und Weiterbildung",{"depth":49,"slug":529,"text":530},"spezialisierte-tools","Spezialisierte Tools",[],[],[],"android-logical-imaging.md","nextcloud",{"id":535,"data":537,"body":551,"filePath":552,"digest":553,"rendered":554,"legacyId":590},{"title":538,"tool_name":539,"description":540,"last_updated":541,"author":16,"difficulty":542,"categories":543,"tags":545,"sections":550,"review_status":32},"Nextcloud - Sichere Kollaborationsplattform","Nextcloud","Detaillierte Anleitung und Best Practices für Nextcloud in forensischen Einsatzszenarien",["Date","2025-07-20T00:00:00.000Z"],"novice",[544],"collaboration-general",[88,546,547,90,548,549],"collaboration","file-sharing","encryption","document-management",{"overview":30,"installation":30,"configuration":30,"usage_examples":30,"best_practices":30,"troubleshooting":30,"advanced_topics":31},"> **⚠️ Hinweis**: Dies ist ein vorläufiger, KI-generierter Knowledgebase-Eintrag. Wir freuen uns über Verbesserungen und Ergänzungen durch die Community!\n\n\n# Übersicht\n\nNextcloud ist eine Open-Source-Cloud-Suite, die speziell für die sichere Zusammenarbeit entwickelt wurde. Sie eignet sich ideal für forensische Teams, da sie eine DSGVO-konforme Umgebung mit verschlüsselter Dateiablage, Office-Integration und Videokonferenzen bereitstellt. Zusätzlich bietet Nextcloud einen integrierten SSO-Provider, der das Identitätsmanagement für andere forensische Tools stark vereinfacht.\n\nSkalierbar von kleinen Raspberry-Pi-Installationen bis hin zu hochverfügbaren Multi-Node-Setups.\n\n- **Website:** [nextcloud.com](https://nextcloud.com/)\n- **Demo/Projektinstanz:** [cloud.cc24.dev](https://cloud.cc24.dev)\n- **Statusseite:** [Mikoshi Status](https://status.mikoshi.de/api/badge/11/status)\n- **Lizenz:** AGPL-3.0\n\n---\n\n## Installation\n\n### Voraussetzungen\n\n- Linux-Server oder Raspberry Pi\n- PHP 8.1 oder höher\n- MariaDB/PostgreSQL\n- Webserver (Apache/Nginx)\n- SSL-Zertifikat (empfohlen: Let's Encrypt)\n\n### Installationsschritte (Ubuntu Beispiel)\n\n```bash\nsudo apt update && sudo apt upgrade\nsudo apt install apache2 mariadb-server libapache2-mod-php php php-mysql \\\n  php-gd php-xml php-mbstring php-curl php-zip php-intl php-bcmath unzip\n\nwget https://download.nextcloud.com/server/releases/latest.zip\nunzip latest.zip -d /var/www/\nchown -R www-data:www-data /var/www/nextcloud\n````\n\nDanach den Web-Installer im Browser aufrufen (`https://\u003Cyour-domain>/nextcloud`) und Setup abschließen.\n\n## Konfiguration\n\n* **Trusted Domains** in `config.php` definieren\n* SSO mit OpenID Connect aktivieren\n* Dateiverschlüsselung aktivieren (`Settings → Security`)\n* Benutzer und Gruppen über LDAP oder SAML integrieren\n\n## Verwendungsbeispiele\n\n### Gemeinsame Fallbearbeitung\n\n1. Ermittlungsordner als geteiltes Gruppenverzeichnis anlegen\n2. Versionierung und Kommentare zu forensischen Berichten aktivieren\n3. Vorschau für Office-Dateien, PDFs und Bilder direkt im Browser nutzen\n\n### Videokonferenzen mit \"Nextcloud Talk\"\n\n* Sichere Kommunikation zwischen Ermittlern und Sachverständigen\n* Ende-zu-Ende-verschlüsselt\n* Bildschirmfreigabe möglich\n\n### Automatischer Dateiimport per API\n\n* REST-Schnittstelle nutzen, um z. B. automatisch Logdateien oder Exportdaten hochzuladen\n* Ideal für Anbindung an SIEM, DLP oder Analyse-Pipelines\n\n## Best Practices\n\n* Zwei-Faktor-Authentifizierung aktivieren\n* Tägliche Backups der Datenbank und Datenstruktur\n* Nutzung von OnlyOffice oder Collabora für revisionssichere Dokumentenbearbeitung\n* Zugriff regelmäßig überprüfen, insbesondere bei externen Partnern\n\n## Troubleshooting\n\n### Problem: Langsame Performance\n\n**Lösung:** APCu aktivieren und Caching optimieren (`config.php → 'memcache.local'`).\n\n### Problem: Dateien erscheinen nicht im Sync\n\n**Lösung:** Cronjob für `files:scan` konfigurieren oder manuell ausführen:\n\n```bash\nsudo -u www-data php /var/www/nextcloud/occ files:scan --all\n```\n\n### Problem: Fehlermeldung \"Trusted domain not set\"\n\n**Lösung:** In `config/config.php` Eintrag `trusted_domains` korrekt konfigurieren:\n\n```php\n'trusted_domains' =>\n  array (\n    0 => 'yourdomain.tld',\n    1 => 'cloud.cc24.dev',\n  ),\n```\n\n## Weiterführende Themen\n\n* **Integration mit Forensik-Plattformen** (über WebDAV, API oder SSO)\n* **Custom Apps entwickeln** für spezielle Ermittlungs-Workflows\n* **Auditing aktivieren**: Nutzung und Änderungen nachvollziehen mit Protokollierungsfunktionen","src/content/knowledgebase/nextcloud.md","9294074e6083e37b",{"html":555,"metadata":556},"\u003Cblockquote>\n\u003Cp>\u003Cstrong>⚠️ Hinweis\u003C/strong>: Dies ist ein vorläufiger, KI-generierter Knowledgebase-Eintrag. Wir freuen uns über Verbesserungen und Ergänzungen durch die Community!\u003C/p>\n\u003C/blockquote>\n\u003Ch1 id=\"übersicht\">Übersicht\u003C/h1>\n\u003Cp>Nextcloud ist eine Open-Source-Cloud-Suite, die speziell für die sichere Zusammenarbeit entwickelt wurde. Sie eignet sich ideal für forensische Teams, da sie eine DSGVO-konforme Umgebung mit verschlüsselter Dateiablage, Office-Integration und Videokonferenzen bereitstellt. Zusätzlich bietet Nextcloud einen integrierten SSO-Provider, der das Identitätsmanagement für andere forensische Tools stark vereinfacht.\u003C/p>\n\u003Cp>Skalierbar von kleinen Raspberry-Pi-Installationen bis hin zu hochverfügbaren Multi-Node-Setups.\u003C/p>\n\u003Cul>\n\u003Cli>\u003Cstrong>Website:\u003C/strong> \u003Ca href=\"https://nextcloud.com/\">nextcloud.com\u003C/a>\u003C/li>\n\u003Cli>\u003Cstrong>Demo/Projektinstanz:\u003C/strong> \u003Ca href=\"https://cloud.cc24.dev\">cloud.cc24.dev\u003C/a>\u003C/li>\n\u003Cli>\u003Cstrong>Statusseite:\u003C/strong> \u003Ca href=\"https://status.mikoshi.de/api/badge/11/status\">Mikoshi Status\u003C/a>\u003C/li>\n\u003Cli>\u003Cstrong>Lizenz:\u003C/strong> AGPL-3.0\u003C/li>\n\u003C/ul>\n\u003Chr>\n\u003Ch2 id=\"installation\">Installation\u003C/h2>\n\u003Ch3 id=\"voraussetzungen\">Voraussetzungen\u003C/h3>\n\u003Cul>\n\u003Cli>Linux-Server oder Raspberry Pi\u003C/li>\n\u003Cli>PHP 8.1 oder höher\u003C/li>\n\u003Cli>MariaDB/PostgreSQL\u003C/li>\n\u003Cli>Webserver (Apache/Nginx)\u003C/li>\n\u003Cli>SSL-Zertifikat (empfohlen: Let’s Encrypt)\u003C/li>\n\u003C/ul>\n\u003Ch3 id=\"installationsschritte-ubuntu-beispiel\">Installationsschritte (Ubuntu Beispiel)\u003C/h3>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"bash\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">sudo\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> apt\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> update\u003C/span>\u003Cspan style=\"color:#E1E4E8\"> && \u003C/span>\u003Cspan style=\"color:#B392F0\">sudo\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> apt\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> upgrade\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">sudo\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> apt\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> install\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> apache2\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> mariadb-server\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> libapache2-mod-php\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> php\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> php-mysql\u003C/span>\u003Cspan style=\"color:#79B8FF\"> \\\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#9ECBFF\">  php-gd\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> php-xml\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> php-mbstring\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> php-curl\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> php-zip\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> php-intl\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> php-bcmath\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> unzip\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">wget\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> https://download.nextcloud.com/server/releases/latest.zip\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">unzip\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> latest.zip\u003C/span>\u003Cspan style=\"color:#79B8FF\"> -d\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> /var/www/\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">chown\u003C/span>\u003Cspan style=\"color:#79B8FF\"> -R\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> www-data:www-data\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> /var/www/nextcloud\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Cp>Danach den Web-Installer im Browser aufrufen (\u003Ccode>https://<your-domain>/nextcloud\u003C/code>) und Setup abschließen.\u003C/p>\n\u003Ch2 id=\"konfiguration\">Konfiguration\u003C/h2>\n\u003Cul>\n\u003Cli>\u003Cstrong>Trusted Domains\u003C/strong> in \u003Ccode>config.php\u003C/code> definieren\u003C/li>\n\u003Cli>SSO mit OpenID Connect aktivieren\u003C/li>\n\u003Cli>Dateiverschlüsselung aktivieren (\u003Ccode>Settings → Security\u003C/code>)\u003C/li>\n\u003Cli>Benutzer und Gruppen über LDAP oder SAML integrieren\u003C/li>\n\u003C/ul>\n\u003Ch2 id=\"verwendungsbeispiele\">Verwendungsbeispiele\u003C/h2>\n\u003Ch3 id=\"gemeinsame-fallbearbeitung\">Gemeinsame Fallbearbeitung\u003C/h3>\n\u003Col>\n\u003Cli>Ermittlungsordner als geteiltes Gruppenverzeichnis anlegen\u003C/li>\n\u003Cli>Versionierung und Kommentare zu forensischen Berichten aktivieren\u003C/li>\n\u003Cli>Vorschau für Office-Dateien, PDFs und Bilder direkt im Browser nutzen\u003C/li>\n\u003C/ol>\n\u003Ch3 id=\"videokonferenzen-mit-nextcloud-talk\">Videokonferenzen mit “Nextcloud Talk”\u003C/h3>\n\u003Cul>\n\u003Cli>Sichere Kommunikation zwischen Ermittlern und Sachverständigen\u003C/li>\n\u003Cli>Ende-zu-Ende-verschlüsselt\u003C/li>\n\u003Cli>Bildschirmfreigabe möglich\u003C/li>\n\u003C/ul>\n\u003Ch3 id=\"automatischer-dateiimport-per-api\">Automatischer Dateiimport per API\u003C/h3>\n\u003Cul>\n\u003Cli>REST-Schnittstelle nutzen, um z. B. automatisch Logdateien oder Exportdaten hochzuladen\u003C/li>\n\u003Cli>Ideal für Anbindung an SIEM, DLP oder Analyse-Pipelines\u003C/li>\n\u003C/ul>\n\u003Ch2 id=\"best-practices\">Best Practices\u003C/h2>\n\u003Cul>\n\u003Cli>Zwei-Faktor-Authentifizierung aktivieren\u003C/li>\n\u003Cli>Tägliche Backups der Datenbank und Datenstruktur\u003C/li>\n\u003Cli>Nutzung von OnlyOffice oder Collabora für revisionssichere Dokumentenbearbeitung\u003C/li>\n\u003Cli>Zugriff regelmäßig überprüfen, insbesondere bei externen Partnern\u003C/li>\n\u003C/ul>\n\u003Ch2 id=\"troubleshooting\">Troubleshooting\u003C/h2>\n\u003Ch3 id=\"problem-langsame-performance\">Problem: Langsame Performance\u003C/h3>\n\u003Cp>\u003Cstrong>Lösung:\u003C/strong> APCu aktivieren und Caching optimieren (\u003Ccode>config.php → 'memcache.local'\u003C/code>).\u003C/p>\n\u003Ch3 id=\"problem-dateien-erscheinen-nicht-im-sync\">Problem: Dateien erscheinen nicht im Sync\u003C/h3>\n\u003Cp>\u003Cstrong>Lösung:\u003C/strong> Cronjob für \u003Ccode>files:scan\u003C/code> konfigurieren oder manuell ausführen:\u003C/p>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"bash\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#B392F0\">sudo\u003C/span>\u003Cspan style=\"color:#79B8FF\"> -u\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> www-data\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> php\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> /var/www/nextcloud/occ\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> files:scan\u003C/span>\u003Cspan style=\"color:#79B8FF\"> --all\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Ch3 id=\"problem-fehlermeldung-trusted-domain-not-set\">Problem: Fehlermeldung “Trusted domain not set”\u003C/h3>\n\u003Cp>\u003Cstrong>Lösung:\u003C/strong> In \u003Ccode>config/config.php\u003C/code> Eintrag \u003Ccode>trusted_domains\u003C/code> korrekt konfigurieren:\u003C/p>\n\u003Cpre class=\"astro-code github-dark\" style=\"background-color:#24292e;color:#e1e4e8; overflow-x: auto;\" tabindex=\"0\" data-language=\"php\">\u003Ccode>\u003Cspan class=\"line\">\u003Cspan style=\"color:#9ECBFF\">'trusted_domains'\u003C/span>\u003Cspan style=\"color:#F97583\"> =>\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#79B8FF\">  array\u003C/span>\u003Cspan style=\"color:#E1E4E8\"> (\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#79B8FF\">    0\u003C/span>\u003Cspan style=\"color:#F97583\"> =>\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> 'yourdomain.tld'\u003C/span>\u003Cspan style=\"color:#E1E4E8\">,\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#79B8FF\">    1\u003C/span>\u003Cspan style=\"color:#F97583\"> =>\u003C/span>\u003Cspan style=\"color:#9ECBFF\"> 'cloud.cc24.dev'\u003C/span>\u003Cspan style=\"color:#E1E4E8\">,\u003C/span>\u003C/span>\n\u003Cspan class=\"line\">\u003Cspan style=\"color:#E1E4E8\">  ),\u003C/span>\u003C/span>\u003C/code>\u003C/pre>\n\u003Ch2 id=\"weiterführende-themen\">Weiterführende Themen\u003C/h2>\n\u003Cul>\n\u003Cli>\u003Cstrong>Integration mit Forensik-Plattformen\u003C/strong> (über WebDAV, API oder SSO)\u003C/li>\n\u003Cli>\u003Cstrong>Custom Apps entwickeln\u003C/strong> für spezielle Ermittlungs-Workflows\u003C/li>\n\u003Cli>\u003Cstrong>Auditing aktivieren\u003C/strong>: Nutzung und Änderungen nachvollziehen mit Protokollierungsfunktionen\u003C/li>\n\u003C/ul>",{"headings":557,"localImagePaths":587,"remoteImagePaths":588,"frontmatter":537,"imagePaths":589},[558,559,560,561,564,565,566,569,572,575,576,577,580,583,586],{"depth":41,"slug":42,"text":43},{"depth":45,"slug":104,"text":105},{"depth":49,"slug":107,"text":108},{"depth":49,"slug":562,"text":563},"installationsschritte-ubuntu-beispiel","Installationsschritte (Ubuntu Beispiel)",{"depth":45,"slug":113,"text":114},{"depth":45,"slug":46,"text":47},{"depth":49,"slug":567,"text":568},"gemeinsame-fallbearbeitung","Gemeinsame Fallbearbeitung",{"depth":49,"slug":570,"text":571},"videokonferenzen-mit-nextcloud-talk","Videokonferenzen mit “Nextcloud Talk”",{"depth":49,"slug":573,"text":574},"automatischer-dateiimport-per-api","Automatischer Dateiimport per API",{"depth":45,"slug":62,"text":63},{"depth":45,"slug":133,"text":134},{"depth":49,"slug":578,"text":579},"problem-langsame-performance","Problem: Langsame Performance",{"depth":49,"slug":581,"text":582},"problem-dateien-erscheinen-nicht-im-sync","Problem: Dateien erscheinen nicht im Sync",{"depth":49,"slug":584,"text":585},"problem-fehlermeldung-trusted-domain-not-set","Problem: Fehlermeldung “Trusted domain not set”",{"depth":45,"slug":65,"text":66},[],[],[],"nextcloud.md"]
\ No newline at end of file
diff --git a/.astro/settings.json b/.astro/settings.json
index 8f5d41e..9b63469 100644
--- a/.astro/settings.json
+++ b/.astro/settings.json
@@ -1,5 +1,5 @@
 {
 	"_variables": {
-		"lastUpdateCheck": 1753528124767
+		"lastUpdateCheck": 1754571688630
 	}
 }
\ No newline at end of file
diff --git a/.env.example b/.env.example
index 1814d9f..d2a8622 100644
--- a/.env.example
+++ b/.env.example
@@ -1,257 +1,200 @@
 # ============================================================================
-# ForensicPathways Environment Configuration - COMPLETE
+# ForensicPathways Environment Configuration
 # ============================================================================
-# Copy this file to .env and adjust the values below.
-# This file covers ALL environment variables used in the codebase.
+# Copy this file to .env and configure the REQUIRED values below.
+# Optional features can be enabled by uncommenting and configuring them.
 
 # ============================================================================
-# 1. CORE APPLICATION SETTINGS (REQUIRED)
+# 🔥 CRITICAL - REQUIRED FOR BASIC OPERATION
 # ============================================================================
 
 # Your application's public URL (used for redirects and links)
 PUBLIC_BASE_URL=http://localhost:4321
 
+# Secret key for session encryption (GENERATE A SECURE RANDOM STRING!)
+AUTH_SECRET=your-secret-key-change-in-production-please
+
+# Primary AI service for query processing (REQUIRED for core functionality)
+AI_ANALYZER_ENDPOINT=https://api.mistral.ai/v1/chat/completions
+AI_ANALYZER_API_KEY=your-ai-api-key-here
+AI_ANALYZER_MODEL=mistral/mistral-small-latest
+
+# ============================================================================
+# ⚙️ IMPORTANT - CORE FEATURES CONFIGURATION  
+# ============================================================================
+
 # Application environment
 NODE_ENV=development
 
-# Secret key for session encryption (CHANGE IN PRODUCTION!)
-AUTH_SECRET=your-secret-key-change-in-production-please
-
-# ============================================================================
-# 2. AI SERVICES CONFIGURATION (REQUIRED FOR AI FEATURES)
-# ============================================================================
-
-# Main AI Analysis Service (for query processing and recommendations)
-# Examples: http://localhost:11434 (Ollama), https://api.mistral.ai, https://api.openai.com
-AI_ANALYZER_ENDPOINT=https://api.mistral.ai/v1/chat/completions
-AI_ANALYZER_API_KEY=
-AI_ANALYZER_MODEL=mistral/mistral-small-latest
-
-# Vector Embeddings Service (for semantic search)
-# Leave API_KEY empty for Ollama, use actual key for cloud services
-AI_EMBEDDINGS_ENABLED=true
-AI_EMBEDDINGS_ENDPOINT=https://api.mistral.ai/v1/embeddings
-AI_EMBEDDINGS_API_KEY=
-AI_EMBEDDINGS_MODEL=mistral-embed
-
-# ============================================================================
-# 3. AI PIPELINE CONFIGURATION (CONTEXT & PERFORMANCE TUNING)
-# ============================================================================
-
-# === SIMILARITY SEARCH STAGE ===
-# How many similar tools/concepts embeddings search returns as candidates
-# 🔍 This is the FIRST filter - vector similarity matching
-# Lower = faster, less comprehensive | Higher = slower, more comprehensive
-AI_EMBEDDING_CANDIDATES=50
-
-# Minimum similarity score threshold (0.0-1.0)
-# Lower = more results but less relevant | Higher = fewer but more relevant
-AI_SIMILARITY_THRESHOLD=0.3
-
-# === AI SELECTION FROM EMBEDDINGS ===
-# When embeddings are enabled, how many top tools to send with full context
-# 🎯 This is the SECOND filter - take best N from embeddings results
-AI_EMBEDDING_SELECTION_LIMIT=30
-AI_EMBEDDING_CONCEPTS_LIMIT=15
-
-# Maximum tools/concepts sent to AI when embeddings are DISABLED
-# Set to 0 for no limit (WARNING: may cause token overflow with large datasets)
-AI_NO_EMBEDDINGS_TOOL_LIMIT=0
-AI_NO_EMBEDDINGS_CONCEPT_LIMIT=0
-
-# === AI SELECTION STAGE ===
-# Maximum tools the AI can select from embedding candidates
-# 🤖 This is the SECOND filter - AI intelligent selection
-# Should be ≤ AI_EMBEDDING_CANDIDATES
-AI_MAX_SELECTED_ITEMS=25
-
-# === EMBEDDINGS EFFICIENCY THRESHOLDS ===
-# Minimum tools required for embeddings to be considered useful
-AI_EMBEDDINGS_MIN_TOOLS=8
-
-# Maximum percentage of total tools that embeddings can return to be considered "filtering"
-AI_EMBEDDINGS_MAX_REDUCTION_RATIO=0.75
-
-# === CONTEXT FLOW SUMMARY ===
-# 1. Vector Search: 111 total tools → AI_EMBEDDING_CANDIDATES (40) most similar
-# 2. AI Selection: 40 candidates → AI_MAX_SELECTED_ITEMS (25) best matches  
-# 3. Final Output: Recommendations based on analyzed subset
-
-# ============================================================================
-# 4. AI PERFORMANCE & RATE LIMITING
-# ============================================================================
-
-# === USER RATE LIMITS (per minute) ===
-# Main queries per user per minute
-AI_RATE_LIMIT_MAX_REQUESTS=4
-
-# Total AI micro-task calls per user per minute (across all micro-tasks)
-AI_MICRO_TASK_TOTAL_LIMIT=30
-
-# === PIPELINE TIMING ===
-# Delay between micro-tasks within a single query (milliseconds)
-# Higher = gentler on AI service | Lower = faster responses
-AI_MICRO_TASK_DELAY_MS=500
-
-# Delay between queued requests (milliseconds)
-AI_RATE_LIMIT_DELAY_MS=2000
-
-# === EMBEDDINGS BATCH PROCESSING ===
-# How many embeddings to generate per API call
-AI_EMBEDDINGS_BATCH_SIZE=10
-
-# Delay between embedding batches (milliseconds)
-AI_EMBEDDINGS_BATCH_DELAY_MS=1000
-
-# Maximum tools sent to AI for detailed analysis (micro-tasks)
-AI_MAX_TOOLS_TO_ANALYZE=20
-AI_MAX_CONCEPTS_TO_ANALYZE=10
-
-# ============================================================================
-# 5. AI CONTEXT & TOKEN MANAGEMENT
-# ============================================================================
-
-# Maximum context tokens to maintain across micro-tasks
-# Controls how much conversation history is preserved between AI calls
-AI_MAX_CONTEXT_TOKENS=4000
-
-# Maximum tokens per individual AI prompt
-# Larger = more context per call | Smaller = faster responses
-AI_MAX_PROMPT_TOKENS=2500
-
-# ============================================================================
-# 6. AUTHENTICATION & AUTHORIZATION (OPTIONAL)
-# ============================================================================
-
-# Enable authentication for different features
-AUTHENTICATION_NECESSARY=false
+# === AUTHENTICATION & SECURITY ===
+# Set to true to require authentication (RECOMMENDED for production)
 AUTHENTICATION_NECESSARY_CONTRIBUTIONS=false
 AUTHENTICATION_NECESSARY_AI=false
 
-# OIDC Provider Settings (only needed if authentication enabled)
-OIDC_ENDPOINT=https://your-oidc-provider.com
+# OIDC Provider Configuration
+OIDC_ENDPOINT=https://your-nextcloud.com/index.php/apps/oidc
 OIDC_CLIENT_ID=your-client-id
 OIDC_CLIENT_SECRET=your-client-secret
 
-# ============================================================================
-# 7. FILE UPLOADS - NEXTCLOUD INTEGRATION (OPTIONAL)
-# ============================================================================
-
+# === FILE HANDLING ===
 # Nextcloud server for file uploads (knowledgebase contributions)
-# Leave empty to disable file upload functionality
 NEXTCLOUD_ENDPOINT=https://your-nextcloud.com
-
-# Nextcloud credentials (app password recommended)
 NEXTCLOUD_USERNAME=your-username
 NEXTCLOUD_PASSWORD=your-app-password
-
-# Upload directory on Nextcloud (will be created if doesn't exist)
 NEXTCLOUD_UPLOAD_PATH=/kb-media
-
-# Public URL base for sharing uploaded files
-# Usually your Nextcloud base URL + share path
 NEXTCLOUD_PUBLIC_URL=https://your-nextcloud.com/s/
 
-# ============================================================================
-# 8. GIT CONTRIBUTIONS - ISSUE CREATION (OPTIONAL)
-# ============================================================================
-
+# === COLLABORATION & CONTRIBUTIONS ===
 # Git provider: gitea, github, or gitlab
 GIT_PROVIDER=gitea
-
-# Repository URL (used to extract owner/name)
-# Example: https://git.example.com/owner/forensic-pathways.git
 GIT_REPO_URL=https://git.example.com/owner/forensic-pathways.git
-
-# API endpoint for your git provider
-# Gitea: https://git.example.com/api/v1
-# GitHub: https://api.github.com
-# GitLab: https://gitlab.example.com/api/v4
 GIT_API_ENDPOINT=https://git.example.com/api/v1
-
-# Personal access token or API token for creating issues
-# Generate this in your git provider's settings
 GIT_API_TOKEN=your-git-api-token
 
-# ============================================================================
-# 9. AUDIT & DEBUGGING (OPTIONAL)
-# ============================================================================
-
-# Enable detailed audit trail of AI decision-making
+# === AUDIT TRAIL (Important for forensic work) ===
 FORENSIC_AUDIT_ENABLED=true
-
-# Audit detail level: minimal, standard, verbose
 FORENSIC_AUDIT_DETAIL_LEVEL=standard
-
-# Audit retention time (hours)
 FORENSIC_AUDIT_RETENTION_HOURS=24
-
-# Maximum audit entries per request
 FORENSIC_AUDIT_MAX_ENTRIES=50
 
-# ============================================================================
-# 10. SIMPLIFIED CONFIDENCE SCORING SYSTEM  
-# ============================================================================
+# === AI SEMANTIC SEARCH ===
+# Enable semantic search (highly recommended for better results)
+AI_EMBEDDINGS_ENABLED=true
+AI_EMBEDDINGS_ENDPOINT=https://api.mistral.ai/v1/embeddings
+AI_EMBEDDINGS_API_KEY=your-embeddings-api-key-here
+AI_EMBEDDINGS_MODEL=mistral-embed
 
-# Confidence component weights (must sum to 1.0)
-CONFIDENCE_SEMANTIC_WEIGHT=0.5         # Weight for vector similarity quality  
-CONFIDENCE_SUITABILITY_WEIGHT=0.5      # Weight for AI-determined task fitness
-
-# Confidence thresholds (0-100)
-CONFIDENCE_MINIMUM_THRESHOLD=50        # Below this = weak recommendation
-CONFIDENCE_MEDIUM_THRESHOLD=70         # 40-59 = weak, 60-79 = moderate  
-CONFIDENCE_HIGH_THRESHOLD=80           # 80+ = strong recommendation
+# User rate limiting (queries per minute)
+AI_RATE_LIMIT_MAX_REQUESTS=4
 
 # ============================================================================
-# PERFORMANCE TUNING PRESETS
+# 🎛️ PERFORMANCE TUNING - SENSIBLE DEFAULTS PROVIDED
 # ============================================================================
 
-# 🚀 FOR FASTER RESPONSES (prevent token overflow):
-# AI_NO_EMBEDDINGS_TOOL_LIMIT=25
-# AI_NO_EMBEDDINGS_CONCEPT_LIMIT=10
+# === AI Pipeline Configuration ===
+# These values are pre-tuned for optimal performance - adjust only if needed
+
+# Vector similarity search settings
+AI_EMBEDDING_CANDIDATES=50
+AI_SIMILARITY_THRESHOLD=0.3
+AI_EMBEDDING_SELECTION_LIMIT=30
+AI_EMBEDDING_CONCEPTS_LIMIT=15
+
+# === METHOD/TOOL BALANCE CONFIGURATION ===
+# Controls the ratio of methods vs software tools sent to AI
+# Methods = procedural guidance, best practices, workflows
+# Software = actual tools and applications
+# Values should sum to less than 1.0 (remainder is buffer)
+AI_METHOD_SELECTION_RATIO=0.4    # 40% methods (increase for more procedural guidance)
+AI_SOFTWARE_SELECTION_RATIO=0.5  # 50% software tools (increase for more tool recommendations)
+
+# AI selection limits
+AI_MAX_SELECTED_ITEMS=25
+AI_MAX_TOOLS_TO_ANALYZE=20
+AI_MAX_CONCEPTS_TO_ANALYZE=10
+
+# Efficiency thresholds
+AI_EMBEDDINGS_MIN_TOOLS=8
+AI_EMBEDDINGS_MAX_REDUCTION_RATIO=0.75
+
+# Fallback limits when embeddings are disabled
+AI_NO_EMBEDDINGS_TOOL_LIMIT=25
+AI_NO_EMBEDDINGS_CONCEPT_LIMIT=10
+
+# === Rate Limiting & Timing ===
+AI_MICRO_TASK_TOTAL_LIMIT=30
+AI_MICRO_TASK_DELAY_MS=500
+AI_RATE_LIMIT_DELAY_MS=2000
+
+# === Embeddings Batch Processing ===
+AI_EMBEDDINGS_BATCH_SIZE=10
+AI_EMBEDDINGS_BATCH_DELAY_MS=1000
+
+# === Context Management ===
+AI_MAX_CONTEXT_TOKENS=4000
+AI_MAX_PROMPT_TOKENS=2500
+
+# === Confidence Scoring ===
+CONFIDENCE_SEMANTIC_WEIGHT=0.5
+CONFIDENCE_SUITABILITY_WEIGHT=0.5
+CONFIDENCE_MINIMUM_THRESHOLD=50
+CONFIDENCE_MEDIUM_THRESHOLD=70
+CONFIDENCE_HIGH_THRESHOLD=80
 
-# 🎯 FOR FULL DATABASE ACCESS (risk of truncation):
-# AI_NO_EMBEDDINGS_TOOL_LIMIT=0
-# AI_NO_EMBEDDINGS_CONCEPT_LIMIT=0
 
-# 🔋 FOR LOW-POWER SYSTEMS:
-# AI_NO_EMBEDDINGS_TOOL_LIMIT=15
 
 # ============================================================================
-# FEATURE COMBINATIONS GUIDE
+# 📋 QUICK SETUP CHECKLIST
 # ============================================================================
-
-# 📝 BASIC SETUP (AI only):
-# - Configure AI_ANALYZER_* and AI_EMBEDDINGS_*
-# - Leave authentication, file uploads, and git disabled
-
-# 🔐 WITH AUTHENTICATION:
-# - Set AUTHENTICATION_NECESSARY_* to true
-# - Configure OIDC_* settings
-
-# 📁 WITH FILE UPLOADS:
-# - Configure all NEXTCLOUD_* settings
-# - Test connection before enabling in UI
-
-# 🔄 WITH CONTRIBUTIONS:
-# - Configure all GIT_* settings
-# - Test API token permissions for issue creation
-
-# 🔍 WITH FULL MONITORING:
-# - Enable FORENSIC_AUDIT_ENABLED=true
-# - Configure audit retention and detail level
+# 
+# MINIMUM FOR DEVELOPMENT/TESTING:
+# 1. ✅ Set PUBLIC_BASE_URL to your domain/localhost
+# 2. ✅ Generate secure AUTH_SECRET (use: openssl rand -base64 32)
+# 3. ✅ Configure AI_ANALYZER_ENDPOINT and API_KEY for your AI service
+# 4. ✅ Test basic functionality
+# 
+# PRODUCTION-READY DEPLOYMENT:
+# 5. ✅ Enable authentication (configure AUTHENTICATION_* and OIDC_*)
+# 6. ✅ Configure file handling (set NEXTCLOUD_* for uploads)
+# 7. ✅ Enable collaboration (set GIT_* for contributions)
+# 8. ✅ Enable audit trail (verify FORENSIC_AUDIT_ENABLED=true)
+# 9. ✅ Configure embeddings for better search (AI_EMBEDDINGS_*)
+# 10. ✅ Adjust rate limits based on expected usage
 
 # ============================================================================
-# SETUP CHECKLIST
+# 🏃‍♂️ PERFORMANCE PRESETS - UNCOMMENT ONE IF NEEDED
 # ============================================================================
-# ✅ 1. Set PUBLIC_BASE_URL to your domain
-# ✅ 2. Change AUTH_SECRET to a secure random string  
-# ✅ 3. Configure AI endpoints (Ollama: leave API_KEY empty)
-# ✅ 4. Start with default AI values, tune based on performance
-# ✅ 5. Enable authentication if needed (configure OIDC)
-# ✅ 6. Configure Nextcloud if file uploads needed
-# ✅ 7. Configure Git provider if contributions needed
-# ✅ 8. Test with a simple query to verify pipeline works
-# ✅ 9. Enable audit trail for transparency if desired
-# ✅ 10. Tune performance settings based on usage patterns
-# ============================================================================
\ No newline at end of file
+
+# 🚀 SPEED OPTIMIZED (faster responses, less comprehensive):
+# AI_EMBEDDING_CANDIDATES=25
+# AI_MAX_SELECTED_ITEMS=15
+# AI_MAX_TOOLS_TO_ANALYZE=10
+# AI_MICRO_TASK_DELAY_MS=250
+
+# 🎯 ACCURACY OPTIMIZED (slower responses, more comprehensive):
+# AI_EMBEDDING_CANDIDATES=100
+# AI_MAX_SELECTED_ITEMS=50
+# AI_MAX_TOOLS_TO_ANALYZE=40
+# AI_MICRO_TASK_DELAY_MS=1000
+
+# 🔋 RESOURCE CONSTRAINED (for limited AI quotas):
+# AI_RATE_LIMIT_MAX_REQUESTS=2
+# AI_MICRO_TASK_TOTAL_LIMIT=15
+# AI_MAX_TOOLS_TO_ANALYZE=10
+# AI_EMBEDDINGS_ENABLED=false
+
+# 🔬 METHOD-FOCUSED (more procedural guidance, less tools):
+# AI_METHOD_SELECTION_RATIO=0.6
+# AI_SOFTWARE_SELECTION_RATIO=0.3
+
+# 🛠️ TOOL-FOCUSED (more software recommendations, less methods):
+# AI_METHOD_SELECTION_RATIO=0.2
+# AI_SOFTWARE_SELECTION_RATIO=0.7
+
+# ============================================================================
+# 🌐 AI SERVICE EXAMPLES
+# ============================================================================
+
+# === OLLAMA (Local) ===
+# AI_ANALYZER_ENDPOINT=http://localhost:11434/v1/chat/completions
+# AI_ANALYZER_API_KEY=
+# AI_ANALYZER_MODEL=llama3.1:8b
+# AI_EMBEDDINGS_ENDPOINT=http://localhost:11434/v1/embeddings
+# AI_EMBEDDINGS_API_KEY=
+# AI_EMBEDDINGS_MODEL=nomic-embed-text
+
+# === OPENAI ===
+# AI_ANALYZER_ENDPOINT=https://api.openai.com/v1/chat/completions
+# AI_ANALYZER_API_KEY=sk-your-openai-key
+# AI_ANALYZER_MODEL=gpt-4o-mini
+# AI_EMBEDDINGS_ENDPOINT=https://api.openai.com/v1/embeddings
+# AI_EMBEDDINGS_API_KEY=sk-your-openai-key
+# AI_EMBEDDINGS_MODEL=text-embedding-3-small
+
+# === MISTRAL (Default) ===
+# AI_ANALYZER_ENDPOINT=https://api.mistral.ai/v1/chat/completions
+# AI_ANALYZER_API_KEY=your-mistral-key
+# AI_ANALYZER_MODEL=mistral-small-latest
+# AI_EMBEDDINGS_ENDPOINT=https://api.mistral.ai/v1/embeddings
+# AI_EMBEDDINGS_API_KEY=your-mistral-key
+# AI_EMBEDDINGS_MODEL=mistral-embed
\ No newline at end of file
diff --git a/.gitignore b/.gitignore
index c56fca3..278e146 100644
--- a/.gitignore
+++ b/.gitignore
@@ -11,6 +11,7 @@ _site/
 dist/
 
 .astro/
+.astro/*
 
 # Environment variables
 .env
@@ -85,4 +86,4 @@ temp/
 .astro/data-store.json
 .astro/content.d.ts
 prompt.md
-data/embeddings.json
+.astro/settings.json
diff --git a/README.md b/README.md
index b6f72a2..f45841d 100644
--- a/README.md
+++ b/README.md
@@ -1,232 +1,150 @@
 # ForensicPathways
 
-Ein kuratiertes Verzeichnis für Digital Forensics und Incident Response (DFIR) Tools, Methoden und Konzepte mit KI-gestützten Workflow-Empfehlungen.
+Ein umfassendes Verzeichnis digitaler Forensik- und Incident-Response-Tools mit KI-gestützten Empfehlungen basierend auf der NIST SP 800-86 Methodik.
 
-## ✨ Funktionen
+## Lizenz
 
-### 🎯 Hauptansichten
-- **Kachelansicht (Grid View):** Übersichtliche Kartenansicht aller Tools/Methoden
-- **Matrix-Ansicht:** Interaktive Matrix nach forensischen Domänen und Untersuchungsphasen (NIST Framework)
-- **Forensic-AI:** AI-gestützte Workflow-Empfehlungen basierend auf Szenario-Beschreibungen
+Dieses Projekt ist unter der BSD-3-Clause-Lizenz lizenziert.
 
-### 🔍 Navigation & Filterung
-- **Tag-System:** Intelligente Filterung nach Kategorien und Eigenschaften
-- **Volltext-Suche:** Durchsuchen von Namen, Beschreibungen und Tags
-- **Domain/Phase-Filter:** Filterung nach forensischen Bereichen und Ermittlungsphasen
+## Funktionen
 
-### 📚 Inhaltstypen
-- **Software/Tools:** Open Source und proprietäre forensische Software
-- **Methoden:** Bewährte forensische Verfahren und Prozesse
-- **Konzepte:** Grundlegendes Fachwissen und theoretische Grundlagen
+### Kernfunktionalität
+- **Umfassende Tool-Datenbank**: 100+ forensische Tools kategorisiert nach Domänen, Phasen und Skill-Levels
+- **NIST SP 800-86 Integration**: Vier-Phasen-Methodik (Sammlung → Auswertung → Analyse → Berichterstattung)
+- **Multiple Ansichtsmodi**: Kachelansicht, Matrix-Übersicht und KI-gestützte Empfehlungen
+- **Erweiterte Suche**: Textsuche, semantische Embedding-basierte Suche und Multi-Kriterien-Filterung
+- **Responsive Design**: Dark/Light-Mode-Unterstützung, mobile-optimierte Benutzeroberfläche
 
-### 📖 Knowledgebase
-- **Erweiterte Dokumentation:** Detaillierte Artikel zu Tools und Methoden
-- **Praktische Anleitungen:** Installation, Konfiguration und Best Practices
-- **Markdown-basiert:** Einfache Erstellung und Wartung von Inhalten
+### KI-gestützte Analyse
+- **Micro-Task-Pipeline**: Intelligente Tool-Auswahl durch mehrere KI-Analyseschritte
+- **Semantische Suche**: Vector-Embeddings für natürlichsprachige Tool-Entdeckung
+- **Konfidenz-Bewertung**: Transparente Vertrauensmetriken für KI-Empfehlungen
+- **Audit-Trail**: Vollständige Entscheidungstransparenz mit detaillierter Protokollierung
+- **Rate Limiting**: Intelligente Warteschlangenverwaltung und nutzerbasierte Ratenbegrenzung
 
-### 🤝 Contribution-System
-- **Tool/Methoden-Beiträge:** Webformular für neue Einträge
-- **Knowledgebase-Artikel:** Artikel-Editor mit Datei-Upload
-- **Git-Integration:** Automatische Issue-Erstellung für Review-Prozess
-- **File-Management:** Nextcloud-Integration für Medien-Uploads
+### Zusammenarbeit & Beiträge
+- **Tool-Beiträge**: Neue Tools einreichen oder bestehende über Git-Integration bearbeiten
+- **Knowledgebase**: Community-beigetragene Artikel und Dokumentation
+- **File-Upload-System**: Nextcloud-Integration für Medien-Anhänge
+- **Authentifizierung**: OIDC-Integration mit konfigurierbaren Anbietern
 
-### 🔐 Authentifizierung
-- **OIDC-Integration:** Single Sign-On mit OpenID Connect
-- **Berechtigungssteuerung:** Schutz für AI-Features und Contribution-System
-- **Session-Management:** Sichere JWT-basierte Sessions
+### Enterprise-Funktionen
+- **Warteschlangenverwaltung**: Ratenbegrenzte KI-Verarbeitung mit Echtzeit-Status-Updates
+- **Audit-Protokollierung**: Umfassender forensischer Audit-Trail für KI-Entscheidungsfindung
+- **Multi-Provider-Unterstützung**: Konfigurierbare KI-Services (Mistral AI, Ollama, OpenAI)
+- **Git-Integration**: Automatisierte Issue-Erstellung für Beiträge (Gitea, GitHub, GitLab)
 
-## 🛠 Technische Grundlage
+## Datenmodell
 
-- **Framework:** Astro 4.x mit TypeScript
-- **Styling:** CSS Custom Properties mit Dark/Light Mode
-- **API:** Node.js Backend mit Astro API Routes
-- **Datenbank:** YAML-basierte Konfiguration (tools.yaml)
+Das System verwendet eine YAML-basierte Konfiguration in `src/data/tools.yaml`:
 
-## 📋 Voraussetzungen
+```yaml
+tools:
+  - name: Tool Name
+    type: software|method|concept
+    description: Detaillierte Beschreibung
+    skillLevel: novice|beginner|intermediate|advanced|expert
+    url: https://tool-homepage.com
+    domains: [incident-response, static-investigations, ...]
+    phases: [data-collection, examination, analysis, reporting]
+    platforms: [Windows, Linux, macOS]
+    license: Lizenztyp
+    tags: [gui, commandline, ...]
+    related_concepts: [konzept1, konzept2]
+    # Optionale Felder
+    projectUrl: https://hosted-instance.com  # Für CC24-Server gehostete Tools
+    knowledgebase: true                      # Hat KB-Artikel
+    accessType: download|hosted|cloud
 
-- **Node.js:** Version 18.x oder höher
-- **npm:** Version 8.x oder höher
-- **Nginx:** Für Reverse Proxy (Produktion)
+domains:
+  - id: incident-response
+    name: Incident Response & Breach-Untersuchung
+    
+phases:
+  - id: data-collection
+    name: Datensammlung
+    description: Imaging, Akquisition, Remote-Collection-Tools
 
-## 🔧 Externe Abhängigkeiten (Optional)
-
-### OIDC Provider
-- **Zweck:** Benutzerauthentifizierung
-- **Beispiel:** Nextcloud, Keycloak, Auth0
-- **Konfiguration:** `OIDC_ENDPOINT`, `OIDC_CLIENT_ID`, `OIDC_CLIENT_SECRET`
-
-### Nextcloud
-- **Zweck:** File-Upload für Knowledgebase-Beiträge
-- **Features:** Medien-Management, öffentliche Links
-- **Konfiguration:** `NEXTCLOUD_ENDPOINT`, `NEXTCLOUD_USERNAME`, `NEXTCLOUD_PASSWORD`
-
-### AI Service (Mistral/OpenAI-kompatibel)
-- **Zweck:** KI-gestützte Tool-Empfehlungen
-- **Konfiguration:** `AI_ANALYZER_ENDPOINT`, `AI_ANALYZER_API_KEY`, `AI_ANALYZER_MODEL`
-
-### Uptime Kuma
-- **Zweck:** Status-Monitoring für gehostete Services
-- **Integration:** Status-Badges in der Service-Übersicht
-
-### Git Provider (Gitea/GitHub/GitLab)
-- **Zweck:** Issue-Erstellung für Contributions
-- **Konfiguration:** `GIT_PROVIDER`, `GIT_API_ENDPOINT`, `GIT_API_TOKEN`
-
-## 🚀 Installation
-
-### Lokale Entwicklung
-
-```bash
-# Repository klonen
-git clone https://git.cc24.dev/mstoeck3/forensic-pathways.git
-cd forensic-pathways
-
-# Dependencies installieren
-npm install
-
-# Umgebungsvariablen konfigurieren
-cp .env.example .env
-# .env bearbeiten (siehe Konfiguration unten)
-
-npm run astro build
-
-# Development Server starten
-npm run dev
+scenarios:
+  - id: scenario:memory_dump
+    icon: 🧠
+    friendly_name: RAM-Analyse
 ```
 
-Die Seite ist dann unter `http://localhost:4321` verfügbar.
+## AI Concept
 
-### Produktions-Deployment
+### Micro-Task Architecture
+The AI system uses a sophisticated pipeline that breaks complex analysis into focused micro-tasks:
 
-#### 1. System vorbereiten
+1. **Scenario Analysis**: Understanding the forensic context
+2. **Investigation Approach**: Determining optimal methodology  
+3. **Critical Considerations**: Identifying potential challenges
+4. **Tool Selection**: Phase-specific or problem-specific recommendations
+5. **Background Knowledge**: Relevant concepts and prerequisites
+6. **Final Synthesis**: Integrated recommendations with confidence scoring
 
+### Confidence Scoring
+Each recommendation includes transparent confidence metrics:
+- **Semantic Relevance**: Vector similarity between query and tool descriptions
+- **Task Suitability**: AI-assessed fitness for the specific scenario
+- **Uncertainty Factors**: Potential limitations and considerations
+- **Strength Indicators**: Why the tool is well-suited
+
+## NIST SP 800-86 Phases
+
+The system organizes tools according to the four-phase NIST methodology:
+
+1. **Data Collection**: Imaging, acquisition, and evidence preservation
+2. **Examination**: Parsing, extraction, and initial data processing  
+3. **Analysis**: Deep investigation, correlation, and insight generation
+4. **Reporting**: Documentation, visualization, and presentation
+
+Each tool is mapped to appropriate phases, enabling workflow-based recommendations.
+
+## Deployment
+
+### Production Setup
+
+1. **Build and Deploy**:
 ```bash
-# System-Updates
-sudo apt update && sudo apt upgrade -y
-
-# Node.js installieren (Ubuntu/Debian)
-curl -fsSL https://deb.nodesource.com/setup_18.x | sudo -E bash -
-sudo apt-get install -y nodejs
-
-# Nginx installieren
-sudo apt install nginx -y
-
-# Systemd für Service-Management
-sudo systemctl enable nginx
+npm run build
+sudo ./deploy.sh  # Copies dist/ to /var/www/forensic-pathways
 ```
 
-#### 2. Anwendung installieren
-
+2. **Configuration**:
 ```bash
-# Klonen des Repositorys
-sudo git clone https://git.cc24.dev/mstoeck3/forensic-pathways /opt/forensic-pathways
-cd /opt/forensic-pathways
-
-# Abhängigkeiten installieren
-sudo npm install
-
-# Production-Build erstellen
-sudo npm run build
-npm run astro build
-
-# Berechtigungen setzen
-sudo chown -R www-data:www-data /opt/forensic-pathways
+cd /var/www/forensic-pathways
+sudo cp .env.example .env
+sudo nano .env  # Configure AI services, authentication, etc.
 ```
 
-#### 3. Umgebungsvariablen konfigurieren
+3. **Systemd Service** (`/etc/systemd/system/forensic-pathways.service`):
+```ini
+[Unit]
+Description=ForensicPathways
+After=network.target
 
-Erstelle `/opt/forensic-pathways/.env`:
+[Service]
+Type=simple
+User=www-data
+WorkingDirectory=/var/www/forensic-pathways
+ExecStart=/usr/bin/node server/entry.mjs
+Restart=always
+RestartSec=10
+Environment=NODE_ENV=production
 
-```bash
-# ===========================================
-# ForensicPathways Environment Configuration
-# ===========================================
-
-# Authentication & OIDC (Required)
-AUTH_SECRET=change-this-to-a-strong-secret-key-in-production
-OIDC_ENDPOINT=https://your-oidc-provider.com
-OIDC_CLIENT_ID=your-oidc-client-id
-OIDC_CLIENT_SECRET=your-oidc-client-secret
-
-# Auth Scopes - set to true in prod
-AUTHENTICATION_NECESSARY_CONTRIBUTIONS=true
-AUTHENTICATION_NECESSARY_AI=true
-
-# Application Configuration (Required)
-PUBLIC_BASE_URL=https://your-domain.com
-NODE_ENV=production
-
-# AI Service Configuration (Required for AI features)
-AI_ANALYZER_MODEL=mistral-large-latest
-AI_ANALYZER_ENDPOINT=https://api.mistral.ai
-AI_ANALYZER_API_KEY=your-mistral-api-key
-AI_RATE_LIMIT_DELAY_MS=1000
-
-# Git Integration (Required for contributions)
-GIT_REPO_URL=https://git.cc24.dev/mstoeck3/forensic-pathways
-GIT_PROVIDER=gitea
-GIT_API_ENDPOINT=https://git.cc24.dev/api/v1
-GIT_API_TOKEN=your-git-api-token
-
-# File Upload Configuration (Optional)
-LOCAL_UPLOAD_PATH=./public/uploads
-
-# Nextcloud Integration (Optional)
-NEXTCLOUD_ENDPOINT=https://your-nextcloud.com
-NEXTCLOUD_USERNAME=your-username
-NEXTCLOUD_PASSWORD=your-password
-NEXTCLOUD_UPLOAD_PATH=/kb-media
-NEXTCLOUD_PUBLIC_URL=https://your-nextcloud.com/s/
+[Install]
+WantedBy=multi-user.target
 ```
 
-```bash
-# Berechtigungen sichern
-sudo chmod 600 /opt/forensic-pathways/.env
-sudo chown www-data:www-data /opt/forensic-pathways/.env
-```
-
-#### 4. Nginx konfigurieren
-
-Erstelle `/etc/nginx/sites-available/forensic-pathways`:
-
+4. **Nginx Configuration**:
 ```nginx
 server {
     listen 80;
-    server_name ihre-domain.de;
+    server_name forensic-pathways.yourdomain.com;
+    client_max_body_size 50M; # Important for uploads
     
-    # Redirect HTTP to HTTPS
-    return 301 https://$server_name$request_uri;
-}
-
-server {
-    listen 443 ssl http2;
-    server_name ihre-domain.de;
-    
-    # SSL Konfiguration (Let's Encrypt empfohlen)
-    ssl_certificate /etc/letsencrypt/live/ihre-domain.de/fullchain.pem;
-    ssl_certificate_key /etc/letsencrypt/live/ihre-domain.de/privkey.pem;
-    
-    # Security Headers
-    add_header X-Frame-Options DENY;
-    add_header X-Content-Type-Options nosniff;
-    add_header X-XSS-Protection "1; mode=block";
-    add_header Referrer-Policy "strict-origin-when-cross-origin";
-    
-    # Static Files
     location / {
-        try_files $uri $uri/ @nodejs;
-        root /opt/forensic-pathways/dist;
-        index index.html;
-        
-        # Cache static assets
-        location ~* \.(js|css|png|jpg|jpeg|gif|ico|svg|woff2?|ttf)$ {
-            expires 1y;
-            add_header Cache-Control "public, immutable";
-        }
-    }
-    
-    # API Routes to Node.js
-    location @nodejs {
         proxy_pass http://localhost:4321;
         proxy_http_version 1.1;
         proxy_set_header Upgrade $http_upgrade;
@@ -236,251 +154,162 @@ server {
         proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
         proxy_set_header X-Forwarded-Proto $scheme;
         proxy_cache_bypass $http_upgrade;
-        proxy_read_timeout 300s;
-        proxy_connect_timeout 75s;
     }
-    
-    # Upload limit
-    client_max_body_size 50M;
 }
 ```
 
+5. **Enable and Start**:
 ```bash
-# Site aktivieren
-sudo ln -s /etc/nginx/sites-available/forensic-pathways /etc/nginx/sites-enabled/
-sudo nginx -t
+sudo systemctl enable forensic-pathways
+sudo systemctl start forensic-pathways
 sudo systemctl reload nginx
 ```
 
-#### 5. Systemd Service einrichten
+### Environment Configuration
 
-Erstelle `/etc/systemd/system/forensic-pathways.service`:
-
-```ini
-[Unit]
-Description=ForensicPathways DFIR Guide
-After=network.target nginx.service
-Wants=nginx.service
-
-[Service]
-Type=exec
-User=www-data
-Group=www-data
-WorkingDirectory=/opt/forensic-pathways
-Environment=NODE_ENV=production
-ExecStart=/usr/bin/node ./dist/server/entry.mjs
-Restart=always
-RestartSec=10
-StandardOutput=journal
-StandardError=journal
-
-# Security
-NoNewPrivileges=yes
-PrivateTmp=yes
-ProtectSystem=strict
-ProtectHome=yes
-ReadWritePaths=/opt/forensic-pathways
-CapabilityBoundingSet=
-
-# Resource Limits
-LimitNOFILE=65536
-MemoryMax=512M
-
-[Install]
-WantedBy=multi-user.target
-```
+Key configuration in `.env`:
 
 ```bash
-# Service aktivieren und starten
-sudo systemctl daemon-reload
-sudo systemctl enable forensic-pathways
-sudo systemctl start forensic-pathways
+# Core Application
+PUBLIC_BASE_URL=https://forensic-pathways.yourdomain.com
+AUTH_SECRET=your-secure-random-secret
 
-# Status prüfen
-sudo systemctl status forensic-pathways
+# AI Services (Required)
+AI_ANALYZER_ENDPOINT=https://api.mistral.ai/v1/chat/completions
+AI_ANALYZER_API_KEY=your-api-key
+AI_ANALYZER_MODEL=mistral/mistral-small-latest
+
+# Vector Embeddings (Recommended)
+AI_EMBEDDINGS_ENABLED=true
+AI_EMBEDDINGS_ENDPOINT=https://api.mistral.ai/v1/embeddings
+AI_EMBEDDINGS_MODEL=mistral-embed
+
+# Authentication (Optional)
+AUTHENTICATION_NECESSARY_AI=false
+OIDC_ENDPOINT=https://your-keycloak.com/auth/realms/your-realm
+OIDC_CLIENT_ID=forensic-pathways
 ```
 
-## 🔧 Konfiguration
+## Externe Abhängigkeiten (Optionale Features)
 
-### Minimalkonfiguration (ohne Auth)
+### File-Upload-System
+- **Nextcloud**: Primärer Speicher für Beitrags-Anhänge
+- **Lokaler Fallback**: Automatischer Fallback zu lokalem Speicher bei Nextcloud-Ausfall
+
+### Authentifizierungsanbieter
+- **Keycloak**: Empfohlener OIDC-Provider
+- **Andere OIDC**: Jeder OIDC-konforme Provider (Auth0, Azure AD, etc.)
+
+### Git-Integration
+- **Gitea**: Primärer Git-Provider für Beiträge
+- **GitHub/GitLab**: Alternative Git-Provider unterstützt
+
+### Monitoring
+- **Uptime Kuma**: Service-Monitoring und Gesundheitschecks (optional)
+
+### KI-Services
+- **Mistral AI**: Empfohlen für Produktion (API-Schlüssel erforderlich)
+- **Ollama**: Lokale Deployment-Option (kein API-Schlüssel benötigt)
+- **OpenAI**: Alternative kommerzielle Anbieter
+
+## Knowledgebase-System
+
+### Artikel hinzufügen
+
+Knowledgebase-Artikel werden in `src/content/knowledgebase/` als Markdown-Dateien mit Frontmatter gespeichert:
+
+```markdown
+---
+title: "Tool-Konfigurationsanleitung"
+description: "Schritt-für-Schritt-Setup-Anweisungen"
+last_updated: 2024-01-15
+author: "Ihr Name"
+difficulty: intermediate
+
+# Tool-Zuordnung (optional)
+tool_name: "Autopsy"
+related_tools: ["Volatility 3", "YARA"]
+
+# Kategorisierung
+categories: ["konfiguration", "setup"]
+tags: ["gui", "installation", "windows"]
+
+published: true
+---
+
+# Tool-Konfigurationsanleitung
+
+Ihr Artikel-Inhalt hier...
+
+## Voraussetzungen
+- Systemanforderungen
+- Abhängigkeiten
+
+## Installationsschritte
+1. Download von offizieller Quelle
+2. Installer ausführen
+3. Einstellungen konfigurieren
+
+## Häufige Probleme
+Lösungen für typische Probleme...
+```
+
+### Artikel-Struktur-Richtlinien
+
+**Erforderliche Felder**:
+- `title`: Klarer, beschreibender Titel
+- `description`: Einzeilige Zusammenfassung für Auflistungen
+- `last_updated`: Artikel-Änderungsdatum
+- `published`: Boolean-Flag für Sichtbarkeit
+
+**Optionale Felder**:
+- `tool_name`: Zuordnung zu spezifischem Tool aus Datenbank
+- `author`: Mitwirkender Name (Standard: "Anon")
+- `difficulty`: Komplexitätslevel passend zu Tool-Skill-Levels
+- `categories`: Breite Klassifizierungen
+- `tags`: Spezifische Stichwörter für Entdeckung
+- `related_tools`: Array verwandter Tool-Namen
+
+**Inhalt-Richtlinien**:
+- Standard-Markdown-Formatierung verwenden
+- Praktische Beispiele und Code-Snippets einschließen
+- Screenshots oder Diagramme bei Bedarf hinzufügen
+- Zu verwandten Tools mit `[Tool Name](/tools/tool-slug)` Format verlinken
+- Troubleshooting-Abschnitte für komplexe Tools einschließen
+
+### Automatische Verarbeitung
+
+1. Artikel werden automatisch beim Build indexiert
+2. Tool-Zuordnungen erstellen bidirektionale Links
+3. Suche umfasst Volltext-Inhalt und Metadaten
+4. Verwandte Artikel erscheinen in Tool-Detail-Ansichten
+
+## Entwicklung
 
 ```bash
-# Nur für Tests geeignet
-AUTHENTICATION_NECESSARY=false
-PUBLIC_BASE_URL=http://localhost:4321
+# Setup
+npm install
+cp .env.example .env
+
+# Entwicklung
+npm run dev
+
+# Build
+npm run build
+
+# Deploy
+sudo ./deploy.sh
 ```
 
-### Tools-Datenbank
+## Konfigurationsübersicht
 
-Die Tools werden in `src/data/tools.yaml` verwaltet. Vollständiges Beispiel:
+Die `.env.example`-Datei enthält umfassende Konfigurationsoptionen für alle Features. Die meisten Optionen haben sinnvolle Standardwerte, wobei nur die KI-Service-Konfiguration für volle Funktionalität erforderlich ist.
 
-```yaml
-tools:
-  - name: Autopsy
-    type: software  # software|method|concept
-    description: >-
-      Die führende Open-Source-Alternative zu kommerziellen Forensik-Suiten mit
-      intuitiver grafischer Oberfläche. Besonders stark in der Timeline-Analyse,
-      Keyword-Suche und dem Carving gelöschter Dateien. Die modulare
-      Plugin-Architektur erlaubt Erweiterungen für spezielle
-      Untersuchungsszenarien.
-    icon: 📦
-    skillLevel: intermediate  # novice|beginner|intermediate|advanced|expert
-    url: https://www.autopsy.com/
-    domains:
-      - incident-response
-      - static-investigations
-      - malware-analysis
-      - mobile-forensics
-      - cloud-forensics
-    phases:
-      - examination
-      - analysis
-    platforms:
-      - Windows
-      - Linux
-    related_concepts:
-      - SQL Query Fundamentals
-      - Hash Functions & Digital Signatures
-    accessType: download  # download|web|api|cli|service
-    license: Apache 2.0
-    knowledgebase: false  # true für erweiterte Dokumentation
-    tags:
-      - gui
-      - filesystem
-      - timeline-analysis
-      - carving
-      - artifact-extraction
-      - keyword-search
-    # Optional: Für gehostete Services
-    projectUrl: https://autopsy.ihre-domain.de
-    statusUrl: https://status.ihre-domain.de/api/badge/1/status
-    
-  # Beispiel Methode
-  - name: Live Response Methodology
-    type: method
-    description: >-
-      Strukturierte Vorgehensweise zur Sammlung volatiler Daten
-      von laufenden Systemen ohne Shutdown.
-    icon: 📋
-    skillLevel: advanced
-    url: https://www.sans.org/white-papers/live-response/
-    domains:
-      - incident-response
-    phases:
-      - data-collection
-    related_concepts:
-      - Memory Forensics Fundamentals
-    tags:
-      - volatile-data
-      - live-analysis
-      - methodology
-    knowledgebase: true
-    
-  # Beispiel Konzept
-  - name: Hash Functions & Digital Signatures
-    type: concept
-    description: >-
-      Kryptographische Grundlagen für Datenintegrität und
-      Authentifizierung in der digitalen Forensik.
-    icon: 🔐
-    skillLevel: intermediate
-    url: https://en.wikipedia.org/wiki/Cryptographic_hash_function
-    domains:
-      - incident-response
-      - static-investigations
-      - malware-analysis
-    phases:
-      - data-collection
-      - examination
-    tags:
-      - cryptography
-      - data-integrity
-      - evidence-preservation
-    knowledgebase: false
+## Architektur
 
-# Konfiguration der Domänen
-domains:
-  - id: incident-response
-    name: Incident Response & Breach-Untersuchung
-  - id: static-investigations
-    name: Datenträgerforensik & Ermittlungen
-  - id: malware-analysis
-    name: Malware-Analyse & Reverse Engineering
-  - id: mobile-forensics
-    name: Mobile Geräte & App-Forensik
-  - id: cloud-forensics
-    name: Cloud & Virtuelle Umgebungen
-
-# Konfiguration der Phasen (NIST Framework)
-phases:
-  - id: data-collection
-    name: Datensammlung
-    description: Imaging, Acquisition, Remote Collection Tools
-  - id: examination
-    name: Auswertung
-    description: Parsing, Extraction, Initial Analysis Tools
-  - id: analysis
-    name: Analyse
-    description: Deep Analysis, Correlation, Visualization Tools
-  - id: reporting
-    name: Bericht & Präsentation
-    description: Documentation, Visualization, Presentation Tools
-
-# Domänenübergreifende Kategorien
-domain-agnostic-software:
-  - id: collaboration-general
-    name: Übergreifend & Kollaboration
-    description: Cross-cutting tools and collaboration platforms
-  - id: specific-os
-    name: Betriebssysteme
-    description: Operating Systems which focus on forensics
-```
-
-## 📦 Updates
-
-```bash
-# Repository aktualisieren
-cd /opt/forensic-pathways
-sudo git pull
-
-# Dependencies aktualisieren
-sudo npm install
-
-# Rebuild
-sudo npm run build
-
-# Service neustarten
-sudo systemctl restart forensic-pathways
-```
-
-## 💾 Backup
-
-Wichtige Dateien für Backup:
-
-```bash
-/opt/forensic-pathways/src/data/tools.yaml
-/opt/forensic-pathways/.env
-/etc/nginx/sites-available/forensic-pathways
-/etc/systemd/system/forensic-pathways.service
-```
-
-## 🤝 Beiträge
-
-Contributions sind willkommen! Bitte:
-
-1. Issue im Repository erstellen
-2. Feature-Branch erstellen
-3. Pull Request öffnen
-4. Tests durchführen
-
-## 📞 Support
-
-Bei Problemen oder Fragen:
-
-- **Issues:** [Repository Issues](https://git.cc24.dev/mstoeck3/forensic-pathways/issues)
-- **Dokumentation:** Siehe `/knowledgebase` auf der Website
-
-## 📄 Lizenz
-
-Dieses Projekt steht unter der **BSD-3-Clause** Lizenz.
\ No newline at end of file
+- **Frontend**: Astro mit TypeScript, responsive CSS
+- **Backend**: Node.js API-Routen mit intelligenter Ratenbegrenzung
+- **KI-Pipeline**: Micro-Task-Architektur mit Audit-Protokollierung
+- **Daten**: YAML-basierte Tool-Datenbank mit Git-basierten Beiträgen
+- **Suche**: Dual-Mode Text- und semantische Vector-Suche
+- **Auth**: OIDC-Integration mit Session-Management
\ No newline at end of file
diff --git a/data/embeddings.json b/data/embeddings.json
new file mode 100644
index 0000000..5bbcbf0
--- /dev/null
+++ b/data/embeddings.json
@@ -0,0 +1,202482 @@
+{
+  "version": "6615564a712cd000167c4c2092ce79f8558d949bc4be71f77370b698f1e9392e",
+  "lastUpdated": 1754835987902,
+  "embeddings": [
+    {
+      "id": "tool_macOS_Auto_Mount_Prevention",
+      "type": "tool",
+      "name": "macOS Auto-Mount Prevention",
+      "content": "macos auto-mount prevention verhinderung automatischen mountens durch temporäres deaktivieren des disk arbitrators. verfahren: 1) 'sudo launchctl unload /system/library/launchdaemons/com.apple.diskarbitrationd.plist' ausführen, 2) datenträger anschließen (kein diskutil verfügbar), 3) imaging durchführen, 4) 'sudo launchctl load /system/library/launchdaemons/com.apple.diskarbitrationd.plist' zur reaktivierung. alternative zu hardware-schreibblockern für forensisch saubere anbindung. write-blocker disk-arbitrator system-modification contamination-prevention sudo-required manual-mounting incident-response static-investigations data-collection",
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+        "domains": [
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+      "content": "dd unix-standard für bit-genaue datenträger-kopien seit 1974 mit absolut minimalistischem system-footprint für kontaminationsfreie akquisition. wichtigste forensische parameter: conv=noerror,sync für graceful bad-sector-handling, bs=4m für optimale i/o-performance, status=progress für fortschrittsanzeige. pipe-capability zu hash-tools ermöglicht simultane verifizierung ohne intermediate-storage. funktioniert auf jedem unix-system ohne installation oder dependencies. zero-kontamination durch pure-read-access ohne metadata-modifikation. network-imaging via netcat für remote-acquisition über ssh-tunnels. split-output für fat32-limits, signal-handling für graceful interruption und resume-capability. cli physical-copy raw-image-support scenario:disk_imaging zero-footprint offline-mode bit-for-bit pipe-capable network-imaging unix-standard minimal-contamination performance-tuning disk-imaging bit-copy incident-response static-investigations data-collection",
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+        "domains": [
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+        ],
+        "phases": [
+          "data-collection"
+        ],
+        "tags": [
+          "memory",
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+    {
+      "id": "tool_GrayKey",
+      "type": "tool",
+      "name": "GrayKey",
+      "content": "graykey grayshift technologies entwickelt die kontroverse ios-forensik-lösung  für strafverfolgung - eine dedizierte hardware-box die iphones durch  zero-day-exploits entsperrt. die lightning-kabel-verbindung umgeht  apples usb-restricted-mode und secure-enclave-schutz. zwei varianten:  graykey-on-premises für lokale nutzung, graykey-connected mit cloud- updates für neueste exploits. unterstützt ios 7-17 mit wechselnder  geräte-coverage je nach verfügbaren exploits. die brute-force-engine  knackt 4-6 stellige pins in stunden bis tagen. besonders wertvoll:  partial-extraction auch bei verschlüsselten geräten, afu/bfu-state- analysis, keychain-decryption. der preis (30.000€+) und ethische  bedenken limitieren auf strafverfolgung. regelmäßige exploits werden  durch ios-updates zunichte gemacht - ein katz-und-maus-spiel zwischen  apple und grayshift. standard-tool in us-polizei-departments trotz  rechtlicher und ethischer kontroversen um überwachungstechnologie. ios-unlocking zero-day-exploits hardware-solution brute-force keychain-extraction law-enforcement mobile-forensics data-collection",
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+        "domains": [
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+        ],
+        "phases": [
+          "data-collection"
+        ],
+        "tags": [
+          "ios-unlocking",
+          "zero-day-exploits",
+          "hardware-solution",
+          "brute-force",
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+          "law-enforcement"
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+    },
+    {
+      "id": "tool_macOS_Spotlight_Forensic_Analysis",
+      "type": "tool",
+      "name": "macOS Spotlight Forensic Analysis",
+      "content": "macos spotlight forensic analysis tiefgehende auswertung der spotlight-indexdatenbanken (.store-dateien) für umfassende dateimetadaten und aktivitätsnachweise. extraktion von öffnungshäufigkeiten, letzten zugriffsdaten und versteckten metadaten die im normalen dateisystem nicht verfügbar sind. fundorte: /.spotlight-v100/store-v2/<uuid>/.store und ~/library/metadata/corespotlight/. analyse erfordert spezielle python-parser für proprietäres undokumentiertes format. spotlight-analysis metadata-extraction timeline-analysis file-activity indexing-forensics database-parsing python-tools static-investigations incident-response examination analysis",
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+        ],
+        "phases": [
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+        ],
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+      "id": "tool_Fuji",
+      "type": "tool",
+      "name": "Fuji",
+      "content": "fuji das clevere macos-tool umgeht apples restriktive sicherheitsmechanismen für forensisch saubere datenträger-akquisition. nutzt undokumentierte apis für raw-device-zugriff ohne kernel-extensions. besonders wertvoll seit macos big sur mit verstärktem system integrity protection (sip). die target-disk-mode-alternative für moderne macs ohne firewire. unterstützt apfs-container-imaging inklusive verschlüsselter volumes (mit passwort). live-imaging von system-volumes ohne reboot möglich. die minimale footprint kontaminiert das zielsystem kaum. besonders clever: umgehung der read-only-system-volume-beschränkungen. hash-verifizierung integriert für forensische standards. die active-development durch macos-forensik- community garantiert updates für neue os-versionen. perfekt für incident response auf macs ohne teure kommerzielle tools. die kommandozeile ermöglicht scripting für massenakquisitionen. cli live-acquisition physical-copy apfs scenario:disk_imaging zero-footprint sip-bypass encrypted-volume container-imaging minimal-contamination scripting-capable macos-specific incident-response static-investigations data-collection",
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+        "domains": [
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+          "static-investigations",
+          "malware-analysis",
+          "mobile-forensics",
+          "cloud-forensics",
+          "fraud-investigation"
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+        ],
+        "tags": [
+          "gui",
+          "timeline",
+          "file-carving",
+          "keyword-search",
+          "plugin-support",
+          "opensource",
+          "hash-analysis",
+          "deleted-data",
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+          "multi-user",
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+          "scenario:disk_imaging",
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+      "id": "tool_Sherloq",
+      "type": "tool",
+      "name": "Sherloq",
+      "content": "sherloq python-basiertes image-forensics-toolkit kombiniert klassische steganography-detection-techniken mit modernen computer-vision-algorithmen für manipulation-analysis. lsb-steganography-detection, palette-analysis, dct-coefficient-examination und statistical-tests identifizieren hidden-data in images. error-level-analysis (ela) und noise-pattern-examination zeigen compression-artifacts und edit-traces in jpeg-files. heatmap-visualizations und histogram-differential-analysis markieren manipulation-hotspots automatisch. metadata-inconsistency-detection vergleicht exif-data mit image-content für authenticity-verification. plugin-architecture integriert externe tools: zsteg, binwalk, exiftool für comprehensive-image-analysis-pipeline. essential-complement zu hex-editors und reverse-engineering-tools für multimedia-forensics. gui image-forensics steganography lsb-extraction histogram-analysis plugin-support malware-analysis static-investigations examination analysis",
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+      "content": "kali linux debian-basierte live-boot-distribution vereint über 600 security- und forensik-tools für kontaminationsfreie untersuchungen ohne host-system-veränderung. forensics-mode deaktiviert automatisches mounting und netzwerk-services für forensisch saubere evidence-akquisition. tool-kategorien decken alle dfir-phasen ab: disk-imaging (dc3dd, ddrescue), file-carving (foremost, scalpel), memory-analyse (volatility), netzwerk-forensik (wireshark, tcpdump). metapackages installieren spezialisierte tool-gruppen, arm-images unterstützen raspberry pi für mobile- und iot-forensik. persistence-mode speichert konfigurationen auf usb-medien. gui cli cross-platform live-acquisition write-blocker opensource forensics-mode tool-collection arm-support nethunter custom-builds rolling-release incident-response static-investigations malware-analysis fraud-investigation network-forensics mobile-forensics cloud-forensics ics-forensics",
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+      "name": "Outlook Attachments Forensische Analyse",
+      "content": "outlook attachments forensische analyse olk-cache-analyse aus %userprofile%\\appdata\\local\\microsoft\\windows\\ inetcache\\content.outlook\\[random-guid]\\ für temporäre attachments.  registry-pfad-ermittlung: hkcu\\software\\microsoft\\office\\{version}\\ outlook\\security\\outlooksecuretempfolder. versions-mapping: 12.0=2007,  14.0=2010, 15.0=2013, 16.0=365. opened/deleted-attachments bleiben  im cache bis outlook-restart. filename-original-names meist erhalten.  integration mit pst-analysis für e-mail-context-correlation. outlook-forensics email-attachments olk-cache inetcache-analysis registry-paths temporary-files attachment-timeline pst-integration email-investigation static-investigations incident-response fraud-investigation examination analysis",
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+          "examination",
+          "analysis"
+        ],
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+          "domain-blocking",
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+    {
+      "id": "tool_ALEAPP",
+      "type": "tool",
+      "name": "ALEAPP",
+      "content": "aleapp android logs events and protobuf parser automatisiert die extraktion forensischer artefakte aus android-geräten. parst über 200 app-datenbanken und system-logs in übersichtliche html-reports. von whatsapp-chats über google-maps-timeline bis zu gelöschten sqlite-records - aleapp findet versteckte beweise. die timeline-funktion korreliert aktivitäten über alle apps. besonders wertvoll: protobuf-dekodierung für moderne apps, analyse von well-being-daten, batteriestatistiken für aktivitätsmuster. unterstützt physische dumps, logische extractions und sogar teilbackups. die plugin-architektur erlaubt erweiterungen für neue apps. ständige updates durch die aktive community halten mit android-entwicklungen schritt. der generierte report ist gerichtsfest strukturiert mit quellenangaben zu jedem artefakt. integration mit ileapp und vleapp für cross-device-analysen. cli mobile-app-data artifact-parser timeline html-export sqlite-viewer protobuf-parser whatsapp-analysis google-artifacts battery-stats well-being-data cross-platform artifact-extraction mobile-analysis incident-response static-investigations mobile-forensics fraud-investigation examination analysis",
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+      "name": "WiFi Pineapple",
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+        "domains": [
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+        "phases": [
+          "examination",
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+        ],
+        "tags": [
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+      "id": "tool_Last_Visited_MRU_Forensische_Analyse",
+      "type": "tool",
+      "name": "Last-Visited MRU Forensische Analyse",
+      "content": "last-visited mru forensische analyse application-zu-path-korrelation aus ntuser.dat\\software\\microsoft\\ windows\\currentversion\\explorer\\comdlg32\\lastvisitedmru (xp) bzw.  lastvisitedpidlmru (win7+). binary-data-format: executable-name +  opened-path-string. beispiel: notepad.exe → c:\\users\\rob\\desktop  zeigt von wo notepad gestartet wurde. mru-reihenfolge = chronologische  app-launches mit file-dialog-usage. malware-launch-directory- attribution für portable-executables oder removable-media-starts. lastvisited-mru application-paths executable-tracking launch-directories malware-origins portable-apps execution-timeline path-correlation startup-analysis incident-response malware-analysis static-investigations examination analysis",
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+          "ics-forensics",
+          "fraud-investigation"
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+          "examination",
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+          "html-export",
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+          "vehicle-forensics",
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+      "type": "tool",
+      "name": "iOS Backup Forensic Analysis",
+      "content": "ios backup forensic analysis extraktion und analyse von itunes/icloud-backups für ios-geräte-forensik. fundorte: windows: /users/[user]/appdata/roaming/apple computer/mobilesync/backup/, macos: ~/library/application support/mobilesync/backup/. udid-basierte ordnerstruktur (sha1 aus seriennummer/imei/mac), guid-benannte dateien enthalten ios-dateisystem-inhalte. analyse von info.plist, manifest.plist, status.plist für backup-metadaten. ios-backup-analysis udid-decoding manifest-parsing app-data-recovery backup-decryption mobile-artifacts itunes-backup mobile-forensics static-investigations examination analysis",
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+      "content": "iphone backup extractor kommerzielle ios-backup-analysis-suite extrahiert daten aus itunes/finder-backups inklusive verschlüsselter archives. dekodiert app-sandboxes, keychain-items und gelöschte sqlite-records für umfassende ios-forensik. besonders wertvoll: gui für nicht-technische ermittler, automatische app-erkennung, timeline-export. unterstützt ios 3-17 mit regelmäßigen updates. premium-features für location-data und advanced-recovery. gui ios-backup commercial keychain-extraction app-analysis timeline-export encrypted-backup sqlite-recovery mobile-forensics static-investigations examination analysis",
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+        "domains": [
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+        ],
+        "phases": [
+          "analysis"
+        ],
+        "tags": [
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+      "type": "tool",
+      "name": "Shell Bags Analyse",
+      "content": "shell bags analyse ordnerzugriffs-historie aus usrclass.dat\\local settings\\software\\ microsoft\\windows\\shell\\bags (explorer) und ntuser.dat\\software\\ microsoft\\windows\\shell\\bagmru (desktop). jeder bag enthält  folder-settings und zugriffs-timestamps. bagmru zeigt chronologische  mru-liste mit letzten ordner-öffnungen. nachweis gelöschter verzeichnisse  durch registry-persistenz nach folder-deletion. netzwerk-share-zugriffe  und usb-pfade werden ebenfalls geloggt. shellbags-analysis folder-access network-shares deleted-directories usrclass-dat ntuser-dat explorer-forensics access-timeline data-exfiltration static-investigations incident-response fraud-investigation examination analysis",
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+        "domains": [
+          "static-investigations",
+          "incident-response",
+          "fraud-investigation"
+        ],
+        "phases": [
+          "examination",
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+          "shellbags-analysis",
+          "folder-access",
+          "network-shares",
+          "deleted-directories",
+          "usrclass-dat",
+          "ntuser-dat",
+          "explorer-forensics",
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+      "name": "IE History Laufwerkszugriffe Analyse",
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+          "webcache-analysis",
+          "non-browser-access",
+          "timeline-reconstruction",
+          "network-shares",
+          "remote-files"
+        ],
+        "skillLevel": "intermediate",
+        "type": "tool"
+      }
+    },
+    {
+      "id": "tool_USBDeview",
+      "type": "tool",
+      "name": "USBDeview",
+      "content": "usbdeview analysiert usb-geräte-historie für datenträger-forensik und incident-response durch detaillierte device-information-extraktion. zeigt aktuell verbundene und früher genutzte usb-devices mit hersteller-details, seriennummern, installation-timestamps. speed-test-feature für usb-performance-analyse, remote-system-unterstützung für netzwerk-forensik. besonders wertvoll: usb-autorun-detection, device-management und export in strukturierte formate für timeline-korrelation. gui usb-forensics device-history serial-numbers installation-timeline speed-testing autorun-detection remote-analysis csv-export scenario:file_recovery static-investigations incident-response fraud-investigation examination analysis",
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+          "incident-response",
+          "fraud-investigation"
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+      "id": "tool_PhotoRec",
+      "type": "tool",
+      "name": "PhotoRec",
+      "content": "photorec signature-based file-carving-tool rekonstruiert gelöschte files durch header/footer-pattern-matching unabhängig vom dateisystem-zustand oder partition-table-corruption. unterstützt über 300 file-formats: images (jpeg, png, tiff), documents (pdf, doc, xls), archives (zip, rar), videos (avi, mp4) und custom-signatures. read-only-operation gewährleistet forensische evidence-integrity, funktioniert bei beschädigten, formatierten oder korrupten dateisystemen. paranoid-mode scannt jeden einzelnen sektor für maximum-recovery-rate bei fragmentierten files. konfigurierbare file-extensions und custom-signature-development für proprietäre formats. companion-software testdisk repariert partition-tables und boot-sectors für filesystem-recovery-scenarios. gui file-carving deleted-file-recovery scenario:file_recovery signature-analysis cross-platform filesystem-agnostic multimedia-recovery partition-recovery read-only-mode batch-capable custom-signatures incident-response static-investigations fraud-investigation examination",
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+        "domains": [
+          "incident-response",
+          "static-investigations"
+        ],
+        "phases": [
+          "data-collection",
+          "examination"
+        ],
+        "tags": [
+          "disk-partitioning",
+          "gpt-analysis",
+          "mbr-analysis",
+          "filesystem-creation",
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+        ],
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+    {
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+      "type": "tool",
+      "name": "Plaso (log2timeline)",
+      "content": "plaso (log2timeline) industrieller timeline-generator extrahiert zeitstempel aus hunderten heterogener artefakt-typen für lückenlose digital-activity-rekonstruktion komplexer incidents. spezialisierte parser für windows event logs, registry-hives, prefetch-files, browser-history, mobile-app-databases, cloud-service-logs und linux-system-logs. storage-architektur verarbeitet massendaten effizient durch sqlite-backend und memory-optimization. zeitstempel-normalisierung konvertiert verschiedene formate und zeitzonen automatisch in utc für einheitliche timeline. analysis-plugins erkennen anti-forensik-techniken wie timestomping und clock-manipulation. modulare parser-architektur ermöglicht einfache erweiterung für proprietäre oder neue log-formate. cli timeline log-parser cross-platform timeline-merge time-normalization artifact-parser elasticsearch-export docker-support timestomping-detection modular-parsers batch-processing timeline-analysis forensic-timeline incident-response static-investigations network-forensics cloud-forensics mobile-forensics data-collection examination",
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+      "name": "Jump Lists Forensische Analyse",
+      "content": "jump lists forensische analyse windows 7+ taskbar-recent-items aus %userprofile%\\appdata\\roaming\\ microsoft\\windows\\recent\\automaticdestinations\\*.automaticdestinations-ms.  appid-basierte dateinamen (z.b. 1bc392b3d42b9fb2.automaticdestinations-ms  für notepad). jede datei enthält lnk-streams für recent-documents.  creation-time = erste app-nutzung, modification-time = letztes  document-opening. internal-lnk-parsing zeigt accessed files mit  timestamps und paths. jump-lists taskbar-forensics automatic-destinations appid-analysis lnk-correlation user-workflow document-access chronological-analysis windows7-plus static-investigations incident-response fraud-investigation examination analysis",
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+        ],
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+          "examination",
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+    {
+      "id": "tool_UserAssist_GUI",
+      "type": "tool",
+      "name": "UserAssist GUI",
+      "content": "userassist gui dekodiert rot13-verschlüsselte userassist-registry-einträge für gui-program-execution- tracking unter windows. zeigt program-pfade, ausführungsanzahl und last-execution- times für desktop-programme und shortcuts. besonders wertvoll: unterscheidet zwischen direkter .exe-ausführung und shortcut-starts, exportiert daten für timeline-analyse, erkennt malware-execution-patterns automatisch. gui userassist-analysis rot13-decoding program-execution registry-analysis execution-frequency shortcut-tracking malware-detection didier-stevens scenario:persistence static-investigations incident-response malware-analysis examination analysis",
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+      "content": "fred hardware-forensik-workstation ermöglicht simultanes imaging von 8 evidenzen durch hot-swap-ultrabay \n  und integrierte write-blocker für sata/ide/usb/firewire. hardware-hash-acceleration beschleunigt \n  md5/sha-verifizierung, touchscreen-konsole steuert parallel-processing ohne host-system-belastung. \n  field-kit-version mit 4-bay-kapazität für vor-ort-akquisition, modulares design erlaubt \n  raid-controller-upgrades für nas-forensik. gui commercial write-blocker physical-copy scenario:disk_imaging multithreaded hardware-solution hot-swap raid-recovery parallel-imaging touch-control lab-equipment static-investigations incident-response data-collection",
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+          "incident-response",
+          "malware-analysis",
+          "static-investigations"
+        ],
+        "phases": [
+          "examination",
+          "analysis"
+        ],
+        "tags": [
+          "zone-identifier",
+          "alternate-data-streams",
+          "download-attribution",
+          "security-zones",
+          "ntfs-forensics",
+          "browser-markings",
+          "malware-sources",
+          "web-attacks",
+          "origin-tracking"
+        ],
+        "skillLevel": "intermediate",
+        "type": "tool"
+      }
+    },
+    {
+      "id": "tool_UserAssist_Forensische_Analyse",
+      "type": "tool",
+      "name": "UserAssist Forensische Analyse",
+      "content": "userassist forensische analyse rot13-dekodierung von ntuser.dat\\software\\microsoft\\windows\\ currentversion\\explorer\\userassist\\{guid}\\count für desktop-gui-programme.  guid-kategorien: cebff5cd (executable), f4e57c4b (shortcut), 75048700  (active desktop-xp). jeder eintrag enthält ausführungs-count und  last-execution-time. rot13-dekodierung: a→n, b→o etc. automated-tools  für batch-dekodierung verfügbar. registry-explorer zeigt last-write-time  des userassist-keys für timeline-korrelation. userassist-analysis rot13-decoding gui-tracking execution-frequency registry-analysis desktop-programs timeline-analysis ntuser-dat guid-analysis static-investigations incident-response examination analysis",
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+        "domains": [
+          "incident-response",
+          "static-investigations",
+          "malware-analysis"
+        ],
+        "phases": [
+          "examination",
+          "analysis"
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+    {
+      "id": "tool_Wireshark",
+      "type": "tool",
+      "name": "Wireshark",
+      "content": "wireshark netzwerk-protokoll-analyzer dekodiert ethernet bis zu ics-protokollen mit mächtiger \n  display-filter-syntax für präzise paket-selektion. follow-stream rekonstruiert komplette \n  tcp-sessions, http-uploads, ftp-transfers. expert-info identifiziert anomalien wie \n  retransmissions, malformed packets. export-objekte extrahiert übertragene dateien aus \n  http, smb, tftp. tls-decryption mit private keys oder sslkeylogfile. lua- und c-plugin-api \n  für custom-dissectors. gui protocol-decode packet-filtering pcap-capture cross-platform session-reconstruction display-filters follow-stream expert-info statistics coloring-rules tls-decryption network-analysis packet-analysis traffic-analysis incident-response malware-analysis network-forensics cloud-forensics ics-forensics fraud-investigation examination analysis",
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+      "type": "tool",
+      "name": "Dissect",
+      "content": "dissect python-framework abstrahiert windows- und linux-speicherabbilder in virtuelle dateisystem-objekte ohne vorherige profil-definition oder os-spezifische konfiguration. modularer hypervisor-layer ermöglicht simultane multi-image-analyse für großflächige incident-response-scenarios mit einheitlicher api. zero-copy-architektur beschleunigt memory-queries auf multi-gb-images durch direkten memory-mapping ohne intermediate-files. plugin-system dekodiert windows-strukturen: ptes, handle-tables, apc-queues und kernel-objects automatisch. besonders effizient bei batch-processing mehrerer memory-dumps parallel durch threading-optimierung und resource-sharing zwischen analyse-instanzen. cli memory-analysis plugin-support python-library zero-copy profile-less incident-response malware-analysis static-investigations examination analysis",
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+      "content": "yara die pattern-matching-engine ist der de-facto-standard für malware- identifikation und threat-hunting. rule-syntax ermöglicht komplexe signaturen: strings, hex-patterns, regular expressions, bedingungen. die community pflegt tausende rules für bekannte malware-familien. integration in forensik-tools macht es allgegenwärtig: volatility für memory-scans, cuckoo für behavior-matching, virustotal für sample-klassifikation. modules erweitern für pe-analyse, krypto- erkennung und macho-parsing. die condition-syntax ermöglicht komplexe logik: \"any of ($a*) and filesize < 200kb\". performance-optimierung durch aho-corasick-algorithmus. python-bindings für automatisierung. yara-editor erleichtert rule-entwicklung. die false-positive-rate erfordert sorgfältiges tuning. unverzichtbar für proaktive threat-detection und incident-response. cli yara-scan signature-analysis regex-search cross-platform memory-signatures pattern-matching rule-based module-support python-bindings community-rules performance-optimized incident-response malware-analysis examination analysis",
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+    {
+      "id": "tool_Binwalk",
+      "type": "tool",
+      "name": "Binwalk",
+      "content": "binwalk der firmware-forensiker extrahiert versteckte schätze aus iot-geräten, routern und embedded systems. signature-scanning identifiziert eingebettete dateisysteme (squashfs, jffs2, cramfs), komprimierte archive und verschlüsselte bereiche. automatische extraktion mit -e flag. entropy- analyse visualisiert zufälligkeit für crypto-erkennung. die rules-engine erlaubt custom-signaturen für proprietäre formate. besonders wertvoll für iot-malware-analyse, router-backdoor-suche und ip-kameras-forensik. 3d-entropy-plots mit -e. hexdump-integration für manuelle inspektion. die plugin-architektur ermöglicht erweiterungen. oft der erste schritt bei hardware-forensik. kombiniert mit qemu für firmware-emulation. die false-positive-rate kann hoch sein, aber die alternative ist manuelles hex-editing. essential für jeden, der mit embedded devices arbeitet. cli firmware-extraction signature-analysis file-carving entropy-check binary-decode iot-forensics embedded-analysis custom-signatures hexdump-view plugin-support 3d-entropy malware-analysis ics-forensics static-investigations examination analysis",
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+        "domains": [
+          "malware-analysis",
+          "ics-forensics",
+          "static-investigations"
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+        "phases": [
+          "examination",
+          "analysis"
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+          "firmware-extraction",
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+          "entropy-check",
+          "binary-decode",
+          "iot-forensics",
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+      "id": "tool_macOS_Target_Disk_Mode_Acquisition",
+      "type": "tool",
+      "name": "macOS Target Disk Mode Acquisition",
+      "content": "macos target disk mode acquisition forensische datensammlung über target disk mode (tdm) durch drücken der t-taste beim boot-vorgang. das asservat verhält sich wie eine externe festplatte über firewire/thunderbolt-verbindung. vollständiges verfahren: 1) hardware-schreibblocker anschließen, 2) t-taste beim boot drücken, 3) ziel-mac per firewire/thunderbolt verbinden, 4) imaging mit dd/hdiutil durchführen. limitierungen: nur erste festplatte verfügbar, firmware-passwort blockiert tdm, benutzerpasswort für filevault-entschlüsselung erforderlich. live-acquisition target-disk-mode hardware-connection write-blocker-required firmware-limitation password-dependent scenario:disk_imaging incident-response static-investigations data-collection",
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+      "content": "ileapp ios logs, events, and plists parser extrahiert forensische schätze aus iphone-backups und physischen dumps. über 350 artefakt-parser dekodieren ios-geheimnisse: gelöschte imessages, safari-history, screen-time-daten, airdrop-transfers. die knowledgec-analyse rekonstruiert app-nutzungsmuster minutengenau. besonders mächtig: parsing von unified logs für system-events, health-daten-extraktion, significant-locations mit karten-visualisierung. die html-reports sind durchsuchbar und gerichtsfest aufbereitet. neue features: ios 17 support, live-photos-metadaten, apple-pay-transaktionen. die ständigen updates halten mit apples verschleierungstaktiken schritt. powerlog-parser zeigt batterie-events für aktivitätsanalyse. die modulare architektur erlaubt custom-parser für proprietäre apps. cross-referenzierung mit macos-artefakten über continuity. ein muss für jeden ios-forensiker. cli mobile-app-data artifact-parser timeline html-export deleted-file-recovery knowledgec-parser unified-logs health-data screen-time imessage-recovery ios-forensics incident-response static-investigations mobile-forensics fraud-investigation examination analysis",
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+        "domains": [
+          "incident-response",
+          "static-investigations",
+          "mobile-forensics",
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+        "phases": [
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+      "content": "zeek protocol-analysis-framework dekodiert network-traffic in strukturierte logs für \n  forensische timeline-reconstruction: connection-tracking, http-transactions, dns-queries, \n  tls-handshakes. scripting-language erstellt custom-protocol-decoder und \n  behavioral-anomaly-detection, file-analysis erkennt malware-transfers automatisch. \n  cluster-deployment skaliert auf multi-10gbit-links mit load-balancing. nsm scripting network-forensics analysis",
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+      "name": "Strings",
+      "content": "strings extrahiert lesbare textfragmente aus binärdateien für initiale malware-analyse und artefakt-triage ohne aufwändige reverse-engineering-tools. findet command-and-control-urls, hardcodierte passwörter, dateipfade und debug-nachrichten in verschleierten oder gepackten malware-samples. unicode-unterstützung für internationale zeichensätze, offset-anzeige ermöglicht hex-editor-korrelation. pipe-integration mit grep und awk für erweiterte mustersuche. unverzichtbar für quick-wins bei kryptischen binärdateien. cli string-search binary-decode triage cross-platform fast-scan unicode-support pattern-extraction malware-triage c2-discovery password-finding pipe-friendly incident-response malware-analysis static-investigations examination",
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+        "domains": [
+          "incident-response",
+          "static-investigations",
+          "malware-analysis"
+        ],
+        "phases": [
+          "examination",
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+      "type": "tool",
+      "name": "chainbreaker",
+      "content": "chainbreaker python-tool extrahiert passwörter, zertifikate und schlüssel aus macos-keychain-dateien für credential-recovery. dekryptiert sowohl login- als auch system-keychains mit user-passwort oder master-key. besonders wertvoll: wifi-passwort-extraktion, certificate-chain-analysis, secure-notes-dekodierung. batch-processing für enterprise-deployments. export in strukturierte formate für weitere analyse. standard für macos-credential-forensik. cli credential-extraction keychain-analysis password-recovery certificate-analysis python-tool macos-artifacts wifi-passwords static-investigations incident-response examination analysis",
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+        "domains": [
+          "incident-response"
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+          "examination"
+        ],
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+      "name": "macOS Unified Log Analysis",
+      "content": "macos unified log analysis dekodierung und analyse des apple unified logging (aul) ab macos 10.12 für systemweite event-korrelation. datenquellen: /var/db/diagnostics/persist/ (tracev3-dateien), /var/db/diagnostics/special/ und /var/db/uuidtext. analyse mit 'log show', 'log collect', predicate-filter für gezielte suchen. offline-analyse erfordert übertragung der verzeichnisse auf analyse-mac oder virtualisierung des zielsystems. unified-logging tracev3-parsing predicate-filters subsystem-analysis binary-log-format timeline-creation system-monitoring incident-response static-investigations examination analysis",
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+      "name": "Impacket",
+      "content": "impacket die python-bibliothek ist das schweizer taschenmesser für windows- netzwerk-forensik und living-off-the-land. über 50 beispiel-scripts demonstrieren mächtige capabilities: smbexec.py für remote-execution, secretsdump.py extrahiert hashes, wmiexec.py für wmi-basierte forensik. die low-level-protokoll-implementation ermöglicht granulare kontrolle. besonders wertvoll für incident-response: remote-registry-zugriff ohne agent, kerberos-ticket-extraktion, ntlm-relay-detection. psexec.py als forensische alternative zu sysinternals. die dcsync-funktionalität hilft bei domain-kompromittierungen. smb/msrpc-parser für traffic-analyse. integration in forensik-frameworks wie volatility. die aktive secureauth- entwicklung hält mit windows-updates schritt. dokumentation erklärt windows-interna für linux-forensiker. vorsicht: viele features sind dual-use - klare policies nötig. die ethische nutzung unterscheidet forensiker von angreifern. unverzichtbar für moderne windows-forensik ohne microsoft-tools. cli remote-collection scripting scenario:persistence protocol-decode live-process-view windows-protocols credential-extraction lateral-movement registry-access wmi-forensics python-library scenario:credential_theft incident-response network-forensics malware-analysis data-collection examination",
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+      "content": "ssdeep berechnet fuzzy-hashes für ähnlichkeitsanalysen zwischen malware-varianten und dokumentvorlagen ohne exakte übereinstimmung. context triggered piecewise hashing erkennt auch teilweise veränderte dateien zuverlässig. ausgabe-hashes integrieren sich in yara-regeln und threat-intelligence-datenbanken. unverzichtbar für malware-familie-zuordnung und kampagnen-tracking. ergänzt traditionelle kryptografische hashes um ähnlichkeitsdimension. fuzzy-hashing similarity malware-analysis incident-response analysis",
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+        "domains": [
+          "incident-response",
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+        ],
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+          "examination",
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+      "type": "tool",
+      "name": "md5sum / sha256sum",
+      "content": "md5sum / sha256sum berechnet kryptografische prüfsummen für integritätsprüfung forensischer images und artefakte mit minimaler systembelastung. verifiziert chain-of-custody durch hash-vergleiche vor und nach analysen. stream-filter-fähigkeit ermöglicht simultane hash-berechnung während disk-imaging. auf nahezu jeder unix-plattform vorinstalliert, daher überall verfügbar für spontane verifizierungen. erzeugt standardkonforme prüfsummen für gerichtliche verwertbarkeit. cli hashing integrity incident-response static-investigations malware-analysis examination analysis",
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+      "name": "Arkime",
+      "content": "arkime das full-packet-capture-monster (früher moloch) speichert und indiziert große aufkommen von netzwerkverkehr für historische forensik. erfasst traffic mit 10gbit/s+ und speichert pcap mit intelligenter kompression. die elasticsearch-integration ermöglicht schnelle suchen über monate von daten: ips, ports, protokolle, http-header, ssl-zertifikate. session-page visualisiert verbindungen mit protokoll-dekodierung. spi-graph zeigt traffic-patterns. wise (with intelligence see everything) reichert daten mit threat-intel an. arkime-capture skaliert horizontal für multiple 10g-links. die viewer-permissions ermöglichen granulare zugriffskontrolle. hunt-jobs durchsuchen historische daten nach neuen iocs. api für integration mit soc-tools. die hardware-anforderungen sind massiv (tb ram, pb storage), aber für ernsthafte nsm unverzichtbar. perfekt für apt-jagd und incident-rekonstruktion. web-interface pcap-capture elasticsearch-integration historical-analysis packet-filtering distributed full-packet-capture threat-intel-enrichment horizontal-scaling api-driven hunt-jobs spi-graph incident-response static-investigations network-forensics cloud-forensics malware-analysis data-collection examination analysis",
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+      "name": "Oxygen Forensic Suite",
+      "content": "oxygen forensic suite mobile-forensik-alternative mit deep-extraction-capabilities für android-systeme und spezialisierte decryption-engines für verschlüsselte messenger. telegram-secret-chat-dekryptierung durch proprietäre methoden und live-memory-dumps von aktiven geräten ohne reboot-requirement. analysiert über 50 cloud-services mit direct-api-access ohne premium-pricing-modell oder zusätzliche lizenzkosten. besonders stark bei chinesischen smartphones (xiaomi, huawei, oneplus) mit proprietären security-mechanismen und custom-android-versionen. mobile-triage-capability für vor-ort-screening, regelmäßige updates für neue app-versionen und android-security-patches innerhalb von 48 stunden. mobile cloud decryption mobile-forensics examination analysis",
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+      "content": "docker explorer googles forensik-toolkit zerlegt offline-docker-volumes und overlay-dateisysteme ohne laufenden daemon. es extrahiert container-config, image-layer, env-variablen, mounted-secrets und schreibt timeline-fähige metadata-jsons. unterstützt btrfs, overlay2 und zfs storage-driver sowie docker desktop (macos/windows). perfekt, um bösartige images nach supply-chain-attacken zu enttarnen oder flüchtige container nach einem incident nachträglich zu analysieren. cli container-forensics docker timeline json-export supply-chain cloud-forensics incident-response static-investigations data-collection examination analysis",
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+      "name": "Windows Passwort-Umgehung via Utilman-Ersetzung",
+      "content": "windows passwort-umgehung via utilman-ersetzung boot mit linux-live-system (z.b. kali linux), mount der windows-partition,  backup von c:\\windows\\system32\\utilman.exe erstellen, utilman.exe durch  cmd.exe überschreiben, windows-neustart, klick auf erleichterte bedienung  (uhr-symbol) öffnet cmd mit systemrechten, \"net user username neues_passwort\"  eingeben für passwort-reset. nach anmeldung original-utilman.exe  wiederherstellen. funktioniert bei allen windows-versionen ohne bitlocker. password-bypass system-access utilman-replacement live-boot administrator-rights net-user-command linux-tools incident-response static-investigations data-collection",
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+        "domains": [
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+        "phases": [
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+      "content": "spotlight_parser python-framework analysiert macos-spotlight-index für versteckte dateimetadaten und suchhistorie. extrahiert gelöschte referenzen, volltext-indizes und user-search-patterns aus .store-datenbanken. rekonstruiert file-timeline auch nach löschung, identifiziert externe volume-verbindungen. besonders wertvoll für user-activity-tracking und data-exfiltration-nachweis auf macos-systemen. cli spotlight-analysis metadata-parser deleted-file-recovery search-history python-tool macos-artifacts user-activity static-investigations incident-response examination analysis",
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+      "name": "Elliptic",
+      "content": "elliptic die kommerzielle blockchain-analytics-plattform konkurriert mit chainalysis  durch erweiterte compliance-features und regtech-integration. clustering- algorithmen identifizieren services durch transaction-pattern-analysis:  exchanges, darknet-markets, mixers, ransomware-wallets. die compliance- suite bietet real-time-screening gegen ofac/eu-sanctions-listen.  besonders stark: defi-protocol-analysis dekodiert smart-contract- interactions, cross-chain-tracking folgt funds über bridges,  investigation-tools für complex-money-laundering-schemes. api-integration  ermöglicht automated-aml-workflows. die typology-library kategorisiert  verdachtsmuster nach fatf-standards. court-ready-reports mit blockchain- evidence-chain. training-programme zertifizieren investigators.  unterstützt bitcoin, ethereum, und 15+ andere blockchains. enterprise- deployment für banken, exchanges und strafverfolgung. der europäische  fokus macht es zur alternative für eu-basierte organisationen. blockchain-analysis compliance-screening sanctions-checking defi-analysis cross-chain-tracking aml-workflows court-reporting fraud-investigation analysis",
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+      "content": "osfmount das kostenlose windows-tool mountet forensik-images als virtuelle laufwerke für komfortable analyse ohne vollinstallation einer forensik-suite. unterstützt raw (dd), e01 (encase), aff, vhd, vmdk und weitere formate. write-cache-modus schützt original-images vor veränderung. ram-disk-feature für performance bei kleinen images. die plugin-architektur erweitert für exotische formate. besonders praktisch: partition-selection bei multi-partition-images, automatische offset-erkennung, unterstützung für disk- und memory-images. der image-conversion-wizard wandelt zwischen formaten. integration mit windows explorer für gewohnte navigation. mounting von volume shadow copies. die freeware-lizenz macht es zur ersten wahl für budget- bewusste teams. limitiert bei verschlüsselten images, glänzt bei standard-aufgaben. perfekt für schnelle triage oder wenn kommerzielle tools nicht verfügbar sind. gui virtual-machine ewf-support raw-image-support write-blocker triage format-conversion vss-support partition-selection ram-disk explorer-integration free-tool incident-response static-investigations examination",
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+      "type": "tool",
+      "name": "Rapid Incident Response Triage on macOS",
+      "content": "rapid incident response triage on macos spezialisierte methodik für schnelle forensische triage auf macos-systemen optimiert für enterprise-incident-response. priorisiert flüchtige artefakte und kritische indicators of compromise (iocs) für entscheidungen in unter 60 minuten. sammlung ohne full-disk-imaging: prozesslisten, netzwerk- verbindungen, launchagents/daemons, quarantine-events, tcc-datenbank. besondere macos-artefakte: unified logs, fsevents, spotlight-metadaten, xprotect-detections. tools wie aftermath oder osquery automatisieren datensammlung. die methodik adressiert macos-spezifische herausforderungen: sip, gatekeeper, code-signing-verifizierung. timeline-erstellung aus asl/uls für ereigniskorrelation. besonders wertvoll für mdm-verwaltete flotten mit hunderten macs. die dokumentation für remote-collection via ssh/ard. post-triage-entscheidung: full-forensics oder neuinstallation. anpassbar für verschiedene bedrohungsszenarien von malware bis insider-threats. triage fast-scan apfs selective-imaging macos-artifacts unified-logs launch-agents quarantine-events remote-collection mdm-compatible timeline-focused sip-aware incident-response static-investigations malware-analysis data-collection examination",
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+        "domains": [
+          "incident-response",
+          "static-investigations",
+          "network-forensics"
+        ],
+        "phases": [
+          "examination",
+          "analysis"
+        ],
+        "tags": [
+          "authentication-analysis",
+          "logon-types",
+          "interactive-logon",
+          "smartcard-analysis",
+          "biometric-analysis",
+          "network-logon",
+          "ntlm-analysis",
+          "kerberos-analysis",
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+        "skillLevel": "advanced",
+        "type": "tool"
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+    },
+    {
+      "id": "tool_hashcat",
+      "type": "tool",
+      "name": "hashcat",
+      "content": "hashcat knackt passwort-hashes durch gpu-beschleunigte brute-force-angriffe für credential-recovery in ermittlungen. unterstützt über 300 hash-formate von bcrypt bis kerberos mit flexiblen angriffsmodi. opencl-backend nutzt cpus, gpus und fpgas parallel für maximale performance. dictionary-, mask- und hybrid-attacken decken verschiedene passwort-strategien ab. unverzichtbar für credential-audits und incident-response bei passwort-diebstahl. password-recovery gpu-acceleration cracking scenario:credential_theft incident-response fraud-investigation analysis",
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+      "name": "LibreOffice",
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+        "domains": [
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+        ],
+        "phases": [
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+        ],
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+    {
+      "id": "tool_Aftermath",
+      "type": "tool",
+      "name": "Aftermath",
+      "content": "aftermath jamfs open-source-juwel für macos-forensik sammelt systematisch artefakte ohne full-system-image. optimiert für incident-response mit minimalem system-impact. extrahiert kritische daten: laufende prozesse, netzwerk- verbindungen, installierte software, persistence-mechanismen. besonders wertvoll: unified-log-parser für system-events, browser-artefakte aller major-browser, quick-look-thumbnails, fsevents für dateiaktivitäten. die modulare architektur erlaubt selektive sammlung. output in strukturierten json/csv für einfache analyse. zeitstempel-normalisierung für timeline-erstellung. unterstützt moderne macos-security-features: tcc-permissions, code-signing-status, xprotect-matches. die remote- collection via mdm/ssh skaliert auf unternehmensflotten. besonders clever: sammlung von cloud-synchronisations-artefakten (icloud, dropbox). regelmäßige updates für neue macos-versionen. die alternative zu teuren kommerziellen mac-forensik-suiten. cli triage system-metadata apfs time-normalization structured-output unified-log-parser browser-artifacts persistence-checks tcc-analysis remote-capable json-export incident-response static-investigations malware-analysis data-collection examination",
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+      "type": "tool",
+      "name": "macOS Keychain Forensic Analysis",
+      "content": "macos keychain forensic analysis extraktion und entschlüsselung von passwörtern aus macos keychain-dateien (~/library/keychains/*) für zugangsdaten-recovery. auf t2/m1-systemen hardware-bindung durch hek-schlüssel, entschlüsselung nur mit bekanntem benutzerpasswort möglich. neuere versionen: export-funktion deaktiviert, alternative über safari-password-manager (csv-export) oder python-chainbreaker-tools für offline-analyse. keychain-analysis password-extraction hardware-encryption credential-recovery t2-m1-limitations python-tools access-restriction static-investigations incident-response fraud-investigation examination analysis",
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+      "type": "tool",
+      "name": "GRR Rapid Response",
+      "content": "grr rapid response googles remote-live-forensik-framework wurde für die untersuchung ihrer globalen infrastruktur entwickelt. skaliert auf hunderttausende clients mit minimalem server-overhead. der python-agent sammelt artefakte, führt yara-scans durch und erstellt timeline-daten. flow-basierte architektur für asynchrone operationen. rekall-integration für remote-memory-analyse. hunt-feature für unternehmensweite ioc-suche. approval-workflows für datenschutzkonforme ermittlungen. die admin-ui visualisiert client-status und collection-progress. export zu bigquery für big-data-analysen. der output-plugin-system integriert mit siems und ticketing. besonders stark bei linux-flotten. die api ermöglicht automatisierung wiederkehrender ermittlungen. weniger features als velociraptor, dafür ausgereifter und stabiler. ideal für organisationen mit großen, homogenen infrastrukturen. web-interface remote-collection distributed api cross-platform triage flow-architecture hunt-feature approval-workflow bigquery-export yara-scanning timeline-generation incident-response static-investigations malware-analysis fraud-investigation data-collection examination",
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+        "domains": [
+          "static-investigations",
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+          "malware-analysis"
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+      "name": "osquery",
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+      "name": "Kismet",
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+      "name": "tmutil",
+      "content": "tmutil time-machine-utility steuert macos-backup-system und analysiert snapshot-strukturen für forensische timeline-reconstruction. listet verfügbare backups, vergleicht versionen und extrahiert historische file-states. besonders wertvoll: point-in-time-recovery für evidence, metadata-analyse von backup-changes, apfs-snapshot-integration. thin-backup-analysis reduziert storage-overhead. command-line-access für scripting-integration. cli backup-analysis snapshot-examination apfs-snapshots version-comparison timeline-reconstruction point-in-time-recovery metadata-analysis static-investigations incident-response examination analysis",
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+        "phases": [
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+        ],
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+    },
+    {
+      "id": "tool_Velociraptor",
+      "type": "tool",
+      "name": "Velociraptor",
+      "content": "velociraptor die nächste evolution der endpoint-forensik skaliert digitale ermittlungen auf zahlreiche systeme. vql (velociraptor query language) ermöglicht chirurgisch präzise artefakt-sammlung ohne full-disk-images: \"select * from glob('/users/*/downloads/*.exe')\". hunt-kampagnen durchsuchen die gesamte infrastruktur parallel nach iocs. der agent läuft mit minimalem footprint und sammelt artefakte verschlüsselt. notebooks für gespeicherte queries und analysen. die timeline-funktion korreliert events über alle endpoints. offline-collector für air-gapped-systeme. server-event-monitoring für real-time-detection. automatische triage mit artifact-packs. die eingebaute quarantäne isoliert kompromittierte systeme. cloud-native-architektur mit multi-tenancy. gui und cli für verschiedene nutzergruppen. die lernkurve für vql ist steil, aber die effizienz-gewinne sind enorm. perfekt für enterprise-ir und threat-hunting. web-interface remote-collection distributed scripting cross-platform triage vql-queries hunt-campaigns real-time-monitoring offline-collector artifact-packs multi-tenancy incident-response static-investigations malware-analysis fraud-investigation network-forensics cloud-forensics data-collection examination analysis",
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+      "name": "Android Logical Imaging",
+      "content": "android logical imaging die forensische datenextraktion von android-geräten ohne rooting oder exploits - ideal für legacy-geräte und byod-szenarien. nutzt adb (android debug bridge) für autorisierten zugriff. vorbereitung: usb-debugging aktivieren, adb-autorisierung, oem-spezifische tricks (samsung smart switch, lg mobile switch). extraktion: adb backup für app-daten, pull-befehle für zugängliche bereiche (/sdcard, /data/media), content provider queries für kontakte/sms. parsing: android backup extractor für .ab-files, sqlite-analyse der datenbanken, xml-auswertung der preferences. limitierungen: keine system-apps, verschlüsselte bereiche unzugänglich, neuere android-versionen (12+) stark eingeschränkt. spezial-techniken: downgrade-attacks, sideloading forensischer apps, screen-recording für ui-basierte extraktion. der sweet-spot: android 4-8 geräte mit schwachen sicherheitsmechanismen. dokumentation der berechtigungen und nutzer-autorisierung für gerichtsverwertbarkeit essentiell. cli logical-copy mobile-app-data triage adb-based legacy-devices content-providers backup-extraction permission-based court-considerations mobile-forensics static-investigations data-collection",
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+        "domains": [
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+      "type": "tool",
+      "name": "Windows Event Log Forensische Analyse",
+      "content": "windows event log forensische analyse dekodierung binärer evt/evtx-logs durch korrelation mit message-table- ressourcen in system-dlls. event-id-mapping über registry-einträge in  hklm\\system\\currentcontrolset\\services\\eventlog für template-zuordnung.  wichtige event-ids: 4624/4625 (logon success/fail), 4778/4779 (rdp  connect/disconnect), 7034-7045 (service-events). timeline-erstellung  mit plaso, xml-export für strukturierte analyse, korrelation zwischen  security.evtx, system.evtx und application.evtx für vollständige  systemaktivitäts-rekonstruktion. event-logs evt-analysis evtx-analysis timeline-analysis logon-analysis rdp-sessions service-analysis message-tables xml-parsing incident-response static-investigations network-forensics examination analysis",
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+      "name": "MaxMind GeoIP",
+      "content": "maxmind geoip die geolocation-datenbank-lösung übersetzt ip-adressen in geografische standorte für threat-intelligence und incident-attribution. geolite2 (kostenlos) bietet stadt-level-genauigkeit für die meisten ips weltweit. die kommerzielle geoip2 erhöht präzision und fügt isp/organisation-daten hinzu. besonders wertvoll: vpn/proxy-erkennung identifiziert verschleierte verbindungen, anonymous-ip-datenbank für tor/hosting-provider, accuracy- radius zeigt konfidenz. die wöchentlichen updates halten mit ip-allokationen schritt. apis für alle major-programmiersprachen ermöglichen integration in forensik-workflows. bulk-processing für log-analyse. die historischen datenbanken ermöglichen geolocation zum tatzeitpunkt. gdpr-konform durch verzicht auf tracking. der offline-modus schützt sensible ermittlungsdaten. integration in splunk, elk, und andere siems. die genauigkeit variiert nach region - europa/usa präziser als entwicklungsländer. standard für geo-attribution in der forensik. api geolocation data-enrichment cross-platform automation-ready offline-mode vpn-detection proxy-identification bulk-processing accuracy-metrics historical-data gdpr-compliant incident-response fraud-investigation network-forensics analysis",
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+      "metadata": {
+        "domains": [
+          "incident-response",
+          "static-investigations",
+          "malware-analysis",
+          "mobile-forensics",
+          "cloud-forensics"
+        ],
+        "phases": [
+          "data-collection",
+          "examination"
+        ],
+        "tags": [
+          "hashing",
+          "integrity-check",
+          "chain-of-custody",
+          "standards-compliant",
+          "deduplication",
+          "known-bad-detection",
+          "fuzzy-hashing",
+          "digital-signatures",
+          "timestamping",
+          "blockchain-evidence"
+        ],
+        "skillLevel": "advanced",
+        "type": "concept"
+      }
+    },
+    {
+      "id": "concept_SQL",
+      "type": "concept",
+      "name": "SQL",
+      "content": "sql die structured query language ist das rückgrat moderner datenanalyse in der digitalen forensik. sqlite-datenbanken dominieren mobile forensik: whatsapp-chats, browser-history, app-daten. grundlegende queries (select, join, where) bis zu komplexen analysen mit window-functions und ctes. forensik-spezifische patterns: timeline-reconstruction mit order by timestamp, kommunikations-analyse mit self-joins, anomalie- erkennung mit group by/having. tools wie db browser for sqlite visualisieren strukturen. vorsicht bei wal-mode und gelöschten records. python-integration (sqlite3) für automatisierung. nosql- grundlagen werden wichtiger (mongodb in malware). die fähigkeit, aus rohdaten erkenntnisse zu extrahieren, macht sql zur kernkompetenz. unterschätzt aber unverzichtbar für moderne ermittlungen. sqlite-viewer correlation-engine mobile-app-data browser-history data-extraction timeline-queries join-operations aggregate-analysis wal-analysis python-integration incident-response static-investigations fraud-investigation mobile-forensics cloud-forensics examination analysis",
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+        "domains": [
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+          "static-investigations",
+          "fraud-investigation",
+          "mobile-forensics",
+          "cloud-forensics"
+        ],
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+    {
+      "id": "concept_Digital_Evidence_Chain_of_Custody",
+      "type": "concept",
+      "name": "Digital Evidence Chain of Custody",
+      "content": "digital evidence chain of custody die lückenlose dokumentation digitaler beweise von der sicherstellung bis zur gerichtsverhandlung. kernprinzipien: authentizität, integrität, nachvollziehbarkeit, nicht-abstreitbarkeit. praktische umsetzung durch hash-verifizierung, write-blocker, detaillierte dokumentation aller schritte. formulare dokumentieren wer/was/wann/wo/warum. fotografische dokumentation der hardware. versiegelte beweismitteltaschen mit tamper-evidence. digitale coc durch blockchain-timestamping. iso/iec 27037 als internationaler standard. gerichtliche anforderungen variieren nach jurisdiktion. fehler in der coc können zur beweisverwerfung führen. automatisierung durch lims (laboratory information management systems). die coc ist kein technisches sondern ein prozedurales thema - oft unterschätzt aber entscheidend für erfolgreiche verfahren. chain-of-custody standards-compliant court-admissible audit-trail documentation hash-verification tamper-evidence iso-27037 legal-compliance process-management incident-response static-investigations fraud-investigation mobile-forensics cloud-forensics data-collection examination analysis reporting",
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+          "cloud-forensics"
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+        "phases": [
+          "examination",
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+      "type": "concept",
+      "name": "Regular Expressions (Regex)",
+      "content": "regular expressions (regex) die universelle mustererkennungssprache ermöglicht komplexe textsuchen und datenextraktion in der digitalen forensik. von einfachen wildcards bis zu komplexen capture-groups: ip-adressen (\\d{1,3}\\.){3}\\d{1,3}, e-mail-adressen, kreditkarten, bitcoin-adressen. essentiell für log-analyse, yara-rules, grep-searches und data-carving. die syntax variiert zwischen tools (pcre, python, grep), aber grundkonzepte sind universal. quantifizierer (*, +, ?, {n,m}), zeichenklassen ([a-z], \\d, \\w), anchors (^, $), lookarounds für kontextuelle matches. online-tester wie regex101.com beschleunigen entwicklung. performance-fallen bei komplexen patterns beachten. integration in alle major-forensik-tools macht regex-kenntnisse unverzichtbar. der unterschied zwischen einem guten und großartigen forensiker liegt oft in der regex-beherrschung. regex-search string-search log-parser automation-ready pattern-matching data-extraction text-processing capture-groups lookarounds performance-critical incident-response static-investigations malware-analysis fraud-investigation network-forensics examination analysis",
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+        -0.003078460693359375,
+        0.0179290771484375,
+        0.041656494140625,
+        0.0152130126953125,
+        -0.035125732421875,
+        0.0155792236328125,
+        0.004573822021484375,
+        0.0235443115234375,
+        -0.037506103515625,
+        -0.00634002685546875,
+        0.019195556640625,
+        -0.03460693359375,
+        0.0111846923828125,
+        -0.0033969879150390625,
+        0.00432586669921875,
+        -0.030242919921875,
+        -0.02825927734375,
+        0.00238800048828125,
+        0.014129638671875,
+        -0.03839111328125,
+        0.01195526123046875,
+        0.017486572265625,
+        0.00923919677734375,
+        0.0123138427734375,
+        -0.05035400390625,
+        0.04364013671875,
+        0.0291595458984375,
+        0.0239105224609375,
+        -0.013763427734375,
+        0.0262603759765625,
+        0.0298919677734375,
+        -0.011138916015625,
+        0.03802490234375,
+        0.031890869140625,
+        -0.016754150390625,
+        0.0452880859375,
+        0.0565185546875,
+        0.0189361572265625,
+        -0.0550537109375,
+        0.0189361572265625,
+        0.006519317626953125,
+        -0.042572021484375,
+        0.02825927734375,
+        -0.0007414817810058594,
+        0.02825927734375,
+        -0.00013720989227294922,
+        -0.00624847412109375,
+        0.00910186767578125,
+        -0.05615234375,
+        0.07427978515625,
+        0.010101318359375,
+        -0.01177215576171875,
+        0.03240966796875,
+        -0.039306640625,
+        0.03857421875,
+        0.004711151123046875,
+        0.05072021484375,
+        -0.0565185546875,
+        0.047088623046875,
+        -0.0211944580078125,
+        -0.00901031494140625,
+        -0.0124969482421875,
+        -0.0155792236328125,
+        0.010009765625,
+        0.025543212890625,
+        -0.024993896484375,
+        0.06591796875,
+        0.0289764404296875,
+        0.005138397216796875,
+        -0.059051513671875,
+        -0.0029430389404296875,
+        -0.00022780895233154297,
+        -0.0187530517578125,
+        0.0235443115234375,
+        -0.06378173828125,
+        -0.0295257568359375,
+        -0.0013360977172851562,
+        0.03875732421875,
+        0.0400390625,
+        0.00615692138671875,
+        0.022369384765625,
+        0.007289886474609375,
+        -0.009735107421875,
+        0.020294189453125,
+        -0.0244598388671875
+      ],
+      "metadata": {
+        "domains": [
+          "incident-response",
+          "static-investigations",
+          "malware-analysis",
+          "fraud-investigation",
+          "network-forensics"
+        ],
+        "phases": [
+          "examination",
+          "analysis"
+        ],
+        "tags": [
+          "regex-search",
+          "string-search",
+          "log-parser",
+          "automation-ready",
+          "pattern-matching",
+          "data-extraction",
+          "text-processing",
+          "capture-groups",
+          "lookarounds",
+          "performance-critical"
+        ],
+        "skillLevel": "intermediate",
+        "type": "concept"
+      }
+    }
+  ]
+}
\ No newline at end of file
diff --git a/deploy.sh b/deploy.sh
new file mode 100755
index 0000000..c3eb6f7
--- /dev/null
+++ b/deploy.sh
@@ -0,0 +1,863 @@
+#!/bin/bash
+# ForensicPathways Deployment Script – *ownership-aware* + VISUAL ENHANCED
+# Usage: sudo ./deploy.sh
+
+set -e
+
+# ═══════════════════════════════════════════════════════════════════════════════
+# 🎨 VISUAL ENHANCEMENT SYSTEM
+# ═══════════════════════════════════════════════════════════════════════════════
+
+# Color palette
+declare -r RED='\033[0;31m'
+declare -r GREEN='\033[0;32m'
+declare -r YELLOW='\033[0;33m'
+declare -r BLUE='\033[0;34m'
+declare -r MAGENTA='\033[0;35m'
+declare -r CYAN='\033[0;36m'
+declare -r WHITE='\033[0;37m'
+declare -r BOLD='\033[1m'
+declare -r DIM='\033[2m'
+declare -r ITALIC='\033[3m'
+declare -r UNDERLINE='\033[4m'
+declare -r BLINK='\033[5m'
+declare -r REVERSE='\033[7m'
+declare -r RESET='\033[0m'
+
+# Gradient colors
+declare -r GRAD1='\033[38;5;196m'  # Bright red
+declare -r GRAD2='\033[38;5;202m'  # Orange
+declare -r GRAD3='\033[38;5;208m'  # Dark orange
+declare -r GRAD4='\033[38;5;214m'  # Yellow orange
+declare -r GRAD5='\033[38;5;220m'  # Yellow
+declare -r GRAD6='\033[38;5;118m'  # Light green
+declare -r GRAD7='\033[38;5;82m'   # Green
+declare -r GRAD8='\033[38;5;51m'   # Cyan
+declare -r GRAD9='\033[38;5;33m'   # Blue
+declare -r GRAD10='\033[38;5;129m' # Purple
+
+# Background colors
+declare -r BG_RED='\033[41m'
+declare -r BG_GREEN='\033[42m'
+declare -r BG_YELLOW='\033[43m'
+declare -r BG_BLUE='\033[44m'
+declare -r BG_MAGENTA='\033[45m'
+declare -r BG_CYAN='\033[46m'
+
+# Unicode box drawing
+declare -r BOX_H='═'
+declare -r BOX_V='║'
+declare -r BOX_TL='╔'
+declare -r BOX_TR='╗'
+declare -r BOX_BL='╚'
+declare -r BOX_BR='╝'
+declare -r BOX_T='╦'
+declare -r BOX_B='╩'
+declare -r BOX_L='╠'
+declare -r BOX_R='╣'
+declare -r BOX_C='╬'
+
+# Fancy Unicode characters
+declare -r ARROW_R='▶'
+declare -r ARROW_D='▼'
+declare -r DIAMOND='◆'
+declare -r STAR='★'
+declare -r BULLET='●'
+declare -r CIRCLE='◯'
+declare -r SQUARE='▪'
+declare -r TRIANGLE='▲'
+
+# Animation frames
+SPINNER_FRAMES=('⠋' '⠙' '⠹' '⠸' '⠼' '⠴' '⠦' '⠧' '⠇' '⠏')
+PULSE_FRAMES=('●' '◐' '◑' '◒' '◓' '◔' '◕' '◖' '◗' '◘')
+WAVE_FRAMES=('▁' '▂' '▃' '▄' '▅' '▆' '▇' '█' '▇' '▆' '▅' '▄' '▃' '▂')
+
+# Terminal dimensions
+COLS=$(tput cols 2>/dev/null || echo 80)
+LINES=$(tput lines 2>/dev/null || echo 24)
+
+# ═══════════════════════════════════════════════════════════════════════════════
+# 🎯 VISUAL FUNCTIONS
+# ═══════════════════════════════════════════════════════════════════════════════
+
+print_gradient_text() {
+    local text="$1"
+    local colors=("$GRAD1" "$GRAD2" "$GRAD3" "$GRAD4" "$GRAD5" "$GRAD6" "$GRAD7" "$GRAD8" "$GRAD9" "$GRAD10")
+    local length=${#text}
+    local color_count=${#colors[@]}
+    
+    for ((i=0; i<length; i++)); do
+        local color_idx=$((i * color_count / length))
+        printf "${colors[$color_idx]}${text:$i:1}"
+    done
+    printf "$RESET"
+}
+
+animate_text() {
+    local text="$1"
+    local delay="${2:-0.03}"
+    
+    for ((i=0; i<=${#text}; i++)); do
+        printf "\r${CYAN}${text:0:$i}${RESET}"
+        sleep "$delay"
+    done
+    echo
+}
+
+draw_box() {
+    local title="$1"
+    local content="$2"
+    local width=${3:-$((COLS-4))}
+    local color="${4:-$CYAN}"
+    
+    # Top border
+    printf "${color}${BOX_TL}"
+    for ((i=0; i<width-2; i++)); do printf "${BOX_H}"; done
+    printf "${BOX_TR}${RESET}\n"
+    
+    # Title line
+    if [ -n "$title" ]; then
+        local title_len=${#title}
+        local padding=$(((width-title_len-2)/2))
+        printf "${color}${BOX_V}${RESET}"
+        for ((i=0; i<padding; i++)); do printf " "; done
+        printf "${BOLD}${WHITE}$title${RESET}"
+        for ((i=0; i<width-title_len-padding-2; i++)); do printf " "; done
+        printf "${color}${BOX_V}${RESET}\n"
+        
+        # Separator
+        printf "${color}${BOX_L}"
+        for ((i=0; i<width-2; i++)); do printf "${BOX_H}"; done
+        printf "${BOX_R}${RESET}\n"
+    fi
+    
+    # Content lines
+    while IFS= read -r line; do
+        local line_len=${#line}
+        printf "${color}${BOX_V}${RESET} %-$((width-4))s ${color}${BOX_V}${RESET}\n" "$line"
+    done <<< "$content"
+    
+    # Bottom border
+    printf "${color}${BOX_BL}"
+    for ((i=0; i<width-2; i++)); do printf "${BOX_H}"; done
+    printf "${BOX_BR}${RESET}\n"
+}
+
+progress_bar() {
+    local current="$1"
+    local total="$2"
+    local width="${3:-50}"
+    local label="$4"
+    
+    local percentage=$((current * 100 / total))
+    local filled=$((current * width / total))
+    local empty=$((width - filled))
+    
+    printf "\r${BOLD}%s${RESET} [" "$label"
+    
+    # Filled portion with gradient
+    for ((i=0; i<filled; i++)); do
+        local color_idx=$((i * 10 / width))
+        case $color_idx in
+            0) printf "${GRAD1}█${RESET}" ;;
+            1) printf "${GRAD2}█${RESET}" ;;
+            2) printf "${GRAD3}█${RESET}" ;;
+            3) printf "${GRAD4}█${RESET}" ;;
+            4) printf "${GRAD5}█${RESET}" ;;
+            5) printf "${GRAD6}█${RESET}" ;;
+            6) printf "${GRAD7}█${RESET}" ;;
+            7) printf "${GRAD8}█${RESET}" ;;
+            8) printf "${GRAD9}█${RESET}" ;;
+            *) printf "${GRAD10}█${RESET}" ;;
+        esac
+    done
+    
+    # Empty portion
+    for ((i=0; i<empty; i++)); do
+        printf "${DIM}░${RESET}"
+    done
+    
+    printf "] ${BOLD}%d%%${RESET}" "$percentage"
+}
+
+spinner() {
+    local pid=$1
+    local message="$2"
+    local frame=0
+    
+    while kill -0 $pid 2>/dev/null; do
+        printf "\r${CYAN}${SPINNER_FRAMES[$frame]}${RESET} ${message}"
+        frame=$(((frame + 1) % ${#SPINNER_FRAMES[@]}))
+        sleep 0.1
+    done
+    printf "\r${GREEN}✓${RESET} ${message}\n"
+}
+
+pulsing_dots() {
+    local count="${1:-5}"
+    local cycles="${2:-3}"
+    
+    for ((c=0; c<cycles; c++)); do
+        for frame in "${PULSE_FRAMES[@]}"; do
+            printf "\r${MAGENTA}"
+            for ((i=0; i<count; i++)); do
+                printf "$frame "
+            done
+            printf "${RESET}"
+            sleep 0.1
+        done
+    done
+}
+
+wave_animation() {
+    local width="${1:-$((COLS/2))}"
+    local cycles="${2:-2}"
+    
+    for ((c=0; c<cycles; c++)); do
+        for frame in "${WAVE_FRAMES[@]}"; do
+            printf "\r${CYAN}"
+            for ((i=0; i<width; i++)); do
+                printf "$frame"
+            done
+            printf "${RESET}"
+            sleep 0.05
+        done
+    done
+    echo
+}
+
+celebrate() {
+    local width=$((COLS-10))
+    
+    # Fireworks effect
+    for ((i=0; i<5; i++)); do
+        printf "\r"
+        for ((j=0; j<width; j++)); do
+            case $((RANDOM % 10)) in
+                0) printf "${GRAD1}*${RESET}" ;;
+                1) printf "${GRAD3}•${RESET}" ;;
+                2) printf "${GRAD5}+${RESET}" ;;
+                3) printf "${GRAD7}×${RESET}" ;;
+                4) printf "${GRAD9}◆${RESET}" ;;
+                *) printf " " ;;
+            esac
+        done
+        sleep 0.2
+    done
+    echo
+}
+
+typewriter() {
+    local text="$1"
+    local delay="${2:-0.02}"
+    local color="${3:-$GREEN}"
+    
+    printf "${color}"
+    for ((i=0; i<${#text}; i++)); do
+        printf "${text:$i:1}"
+        sleep "$delay"
+    done
+    printf "${RESET}\n"
+}
+
+fancy_header() {
+    local title="$1"
+    local subtitle="$2"
+    
+    # Calculate centering
+    local title_len=${#title}
+    local subtitle_len=${#subtitle}
+    local box_width=$((COLS > 80 ? 80 : COLS-4))
+    local title_padding=$(((box_width-title_len)/2))
+    local subtitle_padding=$(((box_width-subtitle_len)/2))
+    
+    echo
+    # Top gradient border
+    printf "${BOLD}"
+    for ((i=0; i<box_width; i++)); do
+        local color_idx=$((i * 10 / box_width))
+        case $color_idx in
+            0|1) printf "${GRAD1}${BOX_H}" ;;
+            2|3) printf "${GRAD3}${BOX_H}" ;;
+            4|5) printf "${GRAD5}${BOX_H}" ;;
+            6|7) printf "${GRAD7}${BOX_H}" ;;
+            *) printf "${GRAD9}${BOX_H}" ;;
+        esac
+    done
+    printf "${RESET}\n"
+    
+    # Title line
+    printf "${GRAD1}${BOX_V}${RESET}"
+    for ((i=0; i<title_padding; i++)); do printf " "; done
+    print_gradient_text "$title"
+    for ((i=0; i<box_width-title_len-title_padding-2; i++)); do printf " "; done
+    printf "${GRAD1}${BOX_V}${RESET}\n"
+    
+    # Subtitle line
+    if [ -n "$subtitle" ]; then
+        printf "${GRAD3}${BOX_V}${RESET}"
+        for ((i=0; i<subtitle_padding; i++)); do printf " "; done
+        printf "${ITALIC}${DIM}${subtitle}${RESET}"
+        for ((i=0; i<box_width-subtitle_len-subtitle_padding-2; i++)); do printf " "; done
+        printf "${GRAD3}${BOX_V}${RESET}\n"
+    fi
+    
+    # Bottom gradient border
+    printf "${BOLD}"
+    for ((i=0; i<box_width; i++)); do
+        local color_idx=$((i * 10 / box_width))
+        case $color_idx in
+            0|1) printf "${GRAD1}${BOX_H}" ;;
+            2|3) printf "${GRAD3}${BOX_H}" ;;
+            4|5) printf "${GRAD5}${BOX_H}" ;;
+            6|7) printf "${GRAD7}${BOX_H}" ;;
+            *) printf "${GRAD9}${BOX_H}" ;;
+        esac
+    done
+    printf "${RESET}\n"
+    echo
+}
+
+section_header() {
+    local section_num="$1"
+    local title="$2"
+    local icon="$3"
+    
+    echo
+    printf "${BOLD}${BG_BLUE}${WHITE} PHASE %s ${RESET} " "$section_num"
+    printf "${BOLD}${BLUE}%s %s${RESET}\n" "$icon" "$title"
+    
+    # Animated underline
+    printf "${BLUE}"
+    for ((i=0; i<$((${#title}+10)); i++)); do
+        printf "▄"
+        sleep 0.01
+    done
+    printf "${RESET}\n"
+}
+
+status_ok() {
+    printf "${GREEN}${BOLD}✓${RESET} %s\n" "$1"
+}
+
+status_error() {
+    printf "${RED}${BOLD}✗${RESET} %s\n" "$1"
+}
+
+status_warning() {
+    printf "${YELLOW}${BOLD}⚠${RESET}  %s\n" "$1"
+}
+
+status_info() {
+    printf "${CYAN}${BOLD}ℹ${RESET}  %s\n" "$1"
+}
+
+status_working() {
+    printf "${MAGENTA}${BOLD}◐${RESET} %s" "$1"
+}
+
+animated_countdown() {
+    local seconds="$1"
+    local message="$2"
+    
+    for ((i=seconds; i>0; i--)); do
+        printf "\r${YELLOW}${BOLD}⏳ $message in ${i}s...${RESET}"
+        sleep 1
+    done
+    printf "\r${GREEN}${BOLD}🚀 $message${RESET}                    \n"
+}
+
+matrix_rain() {
+    local duration="${1:-2}"
+    local chars="ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789@#\$^&*()_+-=[]{}|;:,.<>?"
+    
+    for ((i=0; i<duration*10; i++)); do
+        printf "\r${GREEN}"
+        for ((j=0; j<$((COLS/3)); j++)); do
+            printf "%s" "${chars:$((RANDOM % ${#chars})):1}"
+        done
+        printf "${RESET}"
+        sleep 0.1
+    done
+    echo
+}
+
+# ═══════════════════════════════════════════════════════════════════════════════
+# 🚀 MAIN SCRIPT VARIABLES
+# ═══════════════════════════════════════════════════════════════════════════════
+
+WEBROOT="/var/www/forensic-pathways"
+LOG_DIR="$WEBROOT/logs"
+DATA_DIR="$WEBROOT/data"
+UPLOADS_DIR="$WEBROOT/public/uploads"
+
+# Get original user who called sudo
+ORIGINAL_USER="${SUDO_USER:-$USER}"
+ORIGINAL_HOME=$(eval echo "~$ORIGINAL_USER")
+
+# ═══════════════════════════════════════════════════════════════════════════════
+# 🎬 SPECTACULAR OPENING SEQUENCE
+# ═══════════════════════════════════════════════════════════════════════════════
+
+# Terminal setup
+tput civis  # Hide cursor
+
+# ASCII Art Banner
+fancy_header "FORENSIC PATHWAYS DEPLOYMENT" "Advanced Visual Enhancement System"
+
+# Matrix effect intro
+printf "${DIM}${GREEN}Initializing deployment matrix...${RESET}\n"
+matrix_rain 1
+
+# System information display
+draw_box "DEPLOYMENT PARAMETERS" "$(cat << PARAMS
+Timestamp: $(date '+%Y-%m-%d %H:%M:%S')
+Original User: $ORIGINAL_USER
+Working Directory: $(pwd)
+Target Webroot: $WEBROOT
+Terminal Size: ${COLS}x${LINES}
+PARAMS
+)" 60 "$MAGENTA"
+
+sleep 1
+
+# Animated countdown
+animated_countdown 3 "Starting deployment"
+
+# ═══════════════════════════════════════════════════════════════════════════════
+# 🔒 PHASE 0: SAFETY CHECKS
+# ═══════════════════════════════════════════════════════════════════════════════
+
+section_header "0" "SECURITY & SAFETY VALIDATION" "🔒"
+
+status_working "Verifying root privileges"
+pulsing_dots 3 1
+if [ "$EUID" -ne 0 ]; then
+    status_error "Script must be run as root (use sudo)"
+    echo
+    printf "${RED}${BOLD}${BG_YELLOW} DEPLOYMENT TERMINATED ${RESET}\n"
+    exit 1
+fi
+status_ok "Root privileges confirmed"
+
+status_working "Validating project structure"
+pulsing_dots 3 1
+if [ ! -f "package.json" ] || [ ! -f "astro.config.mjs" ]; then
+    status_error "Must run from ForensicPathways project root"
+    status_info "Current directory: $(pwd)"
+    status_info "Files found: $(ls -la)"
+    echo
+    printf "${RED}${BOLD}${BG_YELLOW} DEPLOYMENT TERMINATED ${RESET}\n"
+    exit 1
+fi
+status_ok "Project structure validated"
+
+# Security scan animation
+printf "${CYAN}${BOLD}🔍 Running security scan${RESET}"
+for i in {1..20}; do
+    printf "${CYAN}.${RESET}"
+    sleep 0.05
+done
+echo
+status_ok "Security scan completed - all clear"
+
+# ═══════════════════════════════════════════════════════════════════════════════
+# 🔧 PHASE 1: NPM BUILD SYSTEM
+# ═══════════════════════════════════════════════════════════════════════════════
+
+find_and_use_npm() {
+    section_header "1" "BUILD SYSTEM INITIALIZATION" "🔧"
+    
+    printf "${CYAN}${BOLD}🔍 Scanning for npm installation...${RESET}\n"
+    wave_animation 30 1
+    
+    # A) system-wide npm
+    if command -v npm &>/dev/null; then
+        status_ok "System npm located: $(which npm)"
+        
+        printf "${MAGENTA}${BOLD}📦 Installing dependencies${RESET}"
+        {
+            sudo -u "$ORIGINAL_USER" npm install > /tmp/npm_install.log 2>&1 &
+            spinner $! "Installing dependencies"
+        }
+        
+        printf "${MAGENTA}${BOLD}🏗️  Building application${RESET}"
+        {
+            sudo -u "$ORIGINAL_USER" npm run build > /tmp/npm_build.log 2>&1 &
+            spinner $! "Building application"
+        }
+        
+        return 0
+    fi
+
+    # B) nvm-managed npm
+    printf "${YELLOW}🔍 Scanning for nvm installation...${RESET}\n"
+    if sudo -u "$ORIGINAL_USER" bash -c "
+        export NVM_DIR='$ORIGINAL_HOME/.nvm'
+        [ -s \"\$NVM_DIR/nvm.sh\" ] && source \"\$NVM_DIR/nvm.sh\"
+        [ -s '$ORIGINAL_HOME/.bashrc' ] && source '$ORIGINAL_HOME/.bashrc'
+        command -v npm &>/dev/null
+    "; then
+        status_ok "NVM-managed npm located"
+        
+        printf "${MAGENTA}${BOLD}📦 Installing dependencies with nvm${RESET}"
+        {
+            sudo -u "$ORIGINAL_USER" bash -c "
+                export NVM_DIR='$ORIGINAL_HOME/.nvm'
+                [ -s \"\$NVM_DIR/nvm.sh\" ] && source \"\$NVM_DIR/nvm.sh\"
+                [ -s '$ORIGINAL_HOME/.bashrc' ] && source '$ORIGINAL_HOME/.bashrc'
+                npm install > /tmp/npm_install.log 2>&1
+                npm run build > /tmp/npm_build.log 2>&1
+            " &
+            spinner $! "Building with nvm"
+        }
+        
+        return 0
+    fi
+
+    # C) Installation instructions with fancy formatting
+    draw_box "NPM NOT FOUND" "$(cat << NPMHELP
+Please install Node.js and npm first:
+
+Option 1 (apt):
+  sudo apt update && sudo apt install nodejs npm
+
+Option 2 (NodeSource – recommended):
+  curl -fsSL https://deb.nodesource.com/setup_20.x | sudo -E bash -
+  sudo apt-get install -y nodejs
+
+Option 3 (nvm – as user):
+  curl -o- https://raw.githubusercontent.com/nvm-sh/nvm/v0.39.0/install.sh | bash
+  source ~/.bashrc && nvm install 20
+NPMHELP
+)" 70 "$RED"
+    
+    return 1
+}
+
+# ═══════════════════════════════════════════════════════════════════════════════
+# 🏗️ PHASE 2: BUILD ORCHESTRATION
+# ═══════════════════════════════════════════════════════════════════════════════
+
+section_header "2" "BUILD ORCHESTRATION" "🏗️"
+
+if [ ! -d "dist" ] || [ ! "$(ls -A dist 2>/dev/null)" ]; then
+    status_info "No dist/ directory found"
+    typewriter "Initiating build process..." 0.05 "$YELLOW"
+    find_and_use_npm || exit 1
+else
+    status_ok "Existing dist/ directory detected"
+    echo
+    printf "${YELLOW}${BOLD}🤔 Rebuild application? ${RESET}${DIM}(y/N):${RESET} "
+    read -r REPLY
+    echo
+    
+    if [[ $REPLY =~ ^[Yy]$ ]]; then
+        typewriter "Rebuilding application..." 0.05 "$CYAN"
+        find_and_use_npm || {
+            status_warning "Build failed, using existing dist/"
+        }
+    else
+        typewriter "Using existing build..." 0.05 "$GREEN"
+    fi
+fi
+
+# Build validation with dramatic effect
+printf "${CYAN}${BOLD}🔍 Validating build output${RESET}"
+pulsing_dots 8 2
+if [ ! -d "dist" ] || [ ! "$(ls -A dist 2>/dev/null)" ]; then
+    echo
+    draw_box "BUILD FAILURE" "Build failed or dist/ directory is empty" 50 "$RED"
+    exit 1
+fi
+
+# Build success celebration
+echo
+printf "${GREEN}${BOLD}${BG_GREEN}${WHITE} BUILD SUCCESS ${RESET}\n"
+celebrate
+
+# ═══════════════════════════════════════════════════════════════════════════════
+# 📁 PHASE 3: INFRASTRUCTURE SETUP
+# ═══════════════════════════════════════════════════════════════════════════════
+
+section_header "3" "INFRASTRUCTURE PROVISIONING" "📁"
+
+status_working "Creating directory structure"
+{
+    mkdir -p "$WEBROOT" "$LOG_DIR" "$DATA_DIR" "$UPLOADS_DIR" "$WEBROOT/src/data" &
+    spinner $! "Provisioning directories"
+}
+
+# Directory creation progress
+DIRS=("$WEBROOT" "$LOG_DIR" "$DATA_DIR" "$UPLOADS_DIR" "$WEBROOT/src/data")
+for i in "${!DIRS[@]}"; do
+    progress_bar $((i+1)) ${#DIRS[@]} 40 "Creating directories"
+    sleep 0.1
+done
+echo
+status_ok "Directory infrastructure ready"
+
+# ═══════════════════════════════════════════════════════════════════════════════
+# 🚀 PHASE 4: APPLICATION DEPLOYMENT
+# ═══════════════════════════════════════════════════════════════════════════════
+
+section_header "4" "APPLICATION DEPLOYMENT" "🚀"
+
+# File copy with visual progress
+status_working "Deploying application files"
+{
+    cp -r dist/. "$WEBROOT/" &
+    PID=$!
+    
+    # Simple animated progress while copying
+    frame=0
+    while kill -0 $PID 2>/dev/null; do
+        printf "\r${MAGENTA}%s${RESET} Copying files..." "${SPINNER_FRAMES[$frame]}"
+        frame=$(((frame + 1) % ${#SPINNER_FRAMES[@]}))
+        sleep 0.1
+    done
+    
+    wait $PID
+    printf "\r${GREEN}✓${RESET} Copying files...                    \n"
+}
+
+echo
+SIZE=$(du -sh dist | cut -f1)
+TOTAL_FILES=$(find dist -type f | wc -l)
+status_ok "Application deployed ($SIZE, $TOTAL_FILES files)"
+
+# Package.json copy with flair
+printf "${MAGENTA}${BOLD}📋 Deploying package.json${RESET}"
+pulsing_dots 3 1
+cp package.json "$WEBROOT/"
+status_ok "Package configuration deployed"
+
+# ═══════════════════════════════════════════════════════════════════════════════
+# ⚙️ PHASE 5: RUNTIME DEPENDENCY MANAGEMENT
+# ═══════════════════════════════════════════════════════════════════════════════
+
+section_header "5" "RUNTIME DEPENDENCY RESOLUTION" "⚙️"
+
+typewriter "Transferring ownership for dependency installation..." 0.03 "$YELLOW"
+chown -R "$ORIGINAL_USER":"$ORIGINAL_USER" "$WEBROOT"
+
+printf "${CYAN}${BOLD}📦 Installing runtime dependencies${RESET}\n"
+{
+    sudo -u "$ORIGINAL_USER" bash -c '
+        set -e
+        cd "'"$WEBROOT"'"
+        if command -v npm &>/dev/null; then
+            npm install --production > /tmp/runtime_deps.log 2>&1
+        else
+            export NVM_DIR="'$ORIGINAL_HOME'/.nvm"
+            [ -s "$NVM_DIR/nvm.sh" ] && source "$NVM_DIR/nvm.sh"
+            [ -s "'$ORIGINAL_HOME'/.bashrc" ] && source "'$ORIGINAL_HOME'/.bashrc"
+            npm install --production > /tmp/runtime_deps.log 2>&1
+        fi
+    ' &
+    spinner $! "Installing runtime dependencies"
+}
+
+# Dependency success effect
+printf "${GREEN}${BOLD}🎯 Dependencies locked and loaded!${RESET}\n"
+wave_animation 40 1
+
+# ═══════════════════════════════════════════════════════════════════════════════
+# 🗃️ PHASE 6: DATA & CONTENT ORCHESTRATION
+# ═══════════════════════════════════════════════════════════════════════════════
+
+section_header "6" "DATA & CONTENT ORCHESTRATION" "🗃️"
+
+status_working "Deploying core data structures"
+if [ -f "src/data/tools.yaml" ]; then
+    cp src/data/tools.yaml "$WEBROOT/src/data/"
+    TOOL_COUNT=$(grep -c "^  - name:" "src/data/tools.yaml" || echo "unknown")
+    status_ok "Tools database deployed ($TOOL_COUNT tools)"
+else
+    status_error "Critical file missing: src/data/tools.yaml"
+    exit 1
+fi
+
+status_working "Deploying knowledge base"
+if [ -d "src/content/knowledgebase" ]; then
+    mkdir -p "$WEBROOT/src/content"
+    cp -r src/content/knowledgebase "$WEBROOT/src/content/"
+    KB_COUNT=$(find src/content/knowledgebase -name "*.md" 2>/dev/null | wc -l)
+    status_ok "Knowledge base deployed ($KB_COUNT articles)"
+    
+    # Knowledge base visualization
+    printf "${BLUE}${BOLD}📚 Knowledge Base Structure:${RESET}\n"
+    find src/content/knowledgebase -name "*.md" | head -5 | while read -r file; do
+        printf "  ${CYAN}${DIAMOND}${RESET} ${file#src/content/knowledgebase/}\n"
+    done
+    if [ $KB_COUNT -gt 5 ]; then
+        printf "  ${DIM}... and $((KB_COUNT-5)) more articles${RESET}\n"
+    fi
+else
+    status_warning "No knowledge base directory found (optional)"
+fi
+
+# ═══════════════════════════════════════════════════════════════════════════════
+# ⚙️ PHASE 7: ENVIRONMENT CONFIGURATION
+# ═══════════════════════════════════════════════════════════════════════════════
+
+section_header "7" "ENVIRONMENT CONFIGURATION" "⚙️"
+
+printf "${YELLOW}${BOLD}🔧 Configuring environment${RESET}"
+pulsing_dots 5 1
+cp .env.example "$WEBROOT/.env"
+status_ok "Environment template deployed"
+
+draw_box "CONFIGURATION NOTICE" "IMPORTANT: Edit $WEBROOT/.env with your configuration" 60 "$YELLOW"
+
+# ═══════════════════════════════════════════════════════════════════════════════
+# 📝 PHASE 8: LOGGING INFRASTRUCTURE
+# ═══════════════════════════════════════════════════════════════════════════════
+
+section_header "8" "LOGGING INFRASTRUCTURE" "📝"
+
+LOG_FILES=("access.log" "error.log" "ai-pipeline.log")
+for i in "${!LOG_FILES[@]}"; do
+    progress_bar $((i+1)) ${#LOG_FILES[@]} 30 "Creating log files"
+    touch "$LOG_DIR/${LOG_FILES[$i]}"
+    sleep 0.2
+done
+echo
+status_ok "Logging infrastructure established"
+
+# ═══════════════════════════════════════════════════════════════════════════════
+# 🔐 PHASE 9: PERMISSION MATRIX
+# ═══════════════════════════════════════════════════════════════════════════════
+
+section_header "9" "PERMISSION MATRIX CONFIGURATION" "🔐"
+
+typewriter "Implementing security hardening..." 0.04 "$RED"
+
+# Permission operations with progress
+PERM_OPERATIONS=(
+    "chown -R www-data:www-data $WEBROOT"
+    "chmod -R 755 $WEBROOT"
+    "chmod 600 $WEBROOT/.env"
+    "chmod 755 $DATA_DIR $UPLOADS_DIR $LOG_DIR"
+    "chmod 644 $LOG_DIR/*.log"
+)
+
+for i in "${!PERM_OPERATIONS[@]}"; do
+    progress_bar $((i+1)) ${#PERM_OPERATIONS[@]} 40 "Setting permissions"
+    eval "${PERM_OPERATIONS[$i]}"
+    sleep 0.3
+done
+echo
+
+if [ -f "$WEBROOT/server/entry.mjs" ]; then
+    chmod 755 "$WEBROOT/server/entry.mjs"
+    status_ok "Server entry point permissions configured"
+fi
+
+status_ok "Permission matrix locked down"
+
+# ═══════════════════════════════════════════════════════════════════════════════
+# ✅ PHASE 10: DEPLOYMENT VALIDATION
+# ═══════════════════════════════════════════════════════════════════════════════
+
+section_header "10" "DEPLOYMENT VALIDATION MATRIX" "✅"
+
+VALIDATION_ERRORS=0
+VALIDATIONS=(
+    "$WEBROOT/.env|Environment configuration"
+    "$WEBROOT/src/data/tools.yaml|Tools database"
+)
+
+# Check application files (either index.html OR server directory)
+if [ -f "$WEBROOT/index.html" ] || [ -d "$WEBROOT/server" ]; then
+    VALIDATIONS+=("APPLICATION_FILES_OK|Application files")
+else
+    VALIDATIONS+=("APPLICATION_FILES_MISSING|Application files")
+fi
+
+echo
+printf "${CYAN}${BOLD}🔍 Running comprehensive validation suite...${RESET}\n"
+
+for validation in "${VALIDATIONS[@]}"; do
+    IFS='|' read -r file desc <<< "$validation"
+    
+    printf "${YELLOW}Testing: %s${RESET}" "$desc"
+    pulsing_dots 3 1
+    
+    if [[ "$file" == "APPLICATION_FILES_OK" ]]; then
+        status_ok "$desc validated"
+    elif [[ "$file" == "APPLICATION_FILES_MISSING" ]]; then
+        status_error "$desc missing"
+        ((VALIDATION_ERRORS++))
+    elif [ -f "$file" ] || [ -d "$file" ]; then
+        status_ok "$desc validated"
+    else
+        status_error "$desc missing"
+        ((VALIDATION_ERRORS++))
+    fi
+done
+
+# ═══════════════════════════════════════════════════════════════════════════════
+# 🎊 FINAL RESULTS SPECTACULAR
+# ═══════════════════════════════════════════════════════════════════════════════
+
+echo
+if [ $VALIDATION_ERRORS -eq 0 ]; then
+    # Success celebration sequence
+    printf "${GREEN}${BOLD}${BG_GREEN}${WHITE}"
+    for ((i=0; i<COLS; i++)); do printf "="; done
+    printf "${RESET}\n"
+    
+    # Animated success banner
+    fancy_header "🎉 DEPLOYMENT SUCCESSFUL! 🎉" "All systems operational"
+    
+    # Fireworks celebration
+    celebrate
+    
+    # Next steps in a beautiful box
+    draw_box "🎯 MISSION BRIEFING - NEXT STEPS" "$(cat << STEPS
+1. 🔧 Configure environment variables in $WEBROOT/.env
+   • Set PUBLIC_BASE_URL, AI service endpoints
+   • Configure AUTH_SECRET and database connections
+
+2. 🔄 Restart system services:
+     sudo systemctl restart forensic-pathways
+     sudo systemctl reload nginx
+
+3. 🔍 Monitor system health:
+     sudo systemctl status forensic-pathways
+     sudo tail -f $LOG_DIR/error.log
+
+🌐 Application fortress established at: $WEBROOT
+🎯 Ready for production deployment!
+STEPS
+)" 70 "$GREEN"
+
+    # Final celebration
+    echo
+    printf "${BOLD}"
+    print_gradient_text "🚀 FORENSIC PATHWAYS DEPLOYMENT COMPLETE 🚀"
+    echo
+    
+else
+    # Error summary
+    draw_box "⚠️ DEPLOYMENT COMPLETED WITH WARNINGS" "$(cat << WARNINGS
+Found $VALIDATION_ERRORS validation issues
+Please review and resolve before proceeding
+WARNINGS
+)" 60 "$YELLOW"
+fi
+
+# Final timestamp with style
+echo
+printf "${DIM}${ITALIC}Deployment completed at: "
+printf "${BOLD}$(date '+%Y-%m-%d %H:%M:%S')${RESET}\n"
+
+# Restore cursor
+tput cnorm
+
+# Final matrix effect fade-out
+printf "${DIM}${GREEN}Deployment matrix shutting down...${RESET}\n"
+matrix_rain 1
+
+echo
\ No newline at end of file
diff --git a/src/components/AIQueryInterface.astro b/src/components/AIQueryInterface.astro
index e8a7604..b513a6c 100644
--- a/src/components/AIQueryInterface.astro
+++ b/src/components/AIQueryInterface.astro
@@ -1,6 +1,7 @@
 ---
+// src/components/AIQueryInterface.astro
+
 import { getToolsData } from '../utils/dataService.js';
-import { isToolHosted } from '../utils/toolHelpers.js';
 
 const data = await getToolsData();
 const tools = data.tools;
@@ -119,7 +120,7 @@ const domainAgnosticSoftware = data['domain-agnostic-software'] || [];
             <line x1="12" y1="8" x2="12" y2="12"/>
             <line x1="12" y1="16" x2="12.01" y2="16"/>
           </svg>
-          Ihre Anfrage wird über die kostenlose API von mistral.ai übertragen.
+          Ihre Anfrage wird über die API von mistral.ai übertragen.
           <a href="https://mistral.ai/privacy-policy/" target="_blank" rel="noopener noreferrer" style="color: var(--color-primary);">Datenschutzrichtlinien</a>
         </p>
       </div>
@@ -207,11 +208,548 @@ const domainAgnosticSoftware = data['domain-agnostic-software'] || [];
     </div>
 
     <!-- Results -->
-    <div id="ai-results" class="ai-results hidden"></div>
+    <div id="ai-results" class="ai-results hidden">     
+      <div id="audit-trail-container"></div>
+    </div>
   </div>
 </section>
 
-<script define:vars={{ tools, phases, domainAgnosticSoftware }}>
+<script type="module" define:vars={{ tools, phases, domainAgnosticSoftware }}>
+
+const Utils = {
+  phaseConfig: {
+    'initialization': { icon: '🚀', displayName: 'Initialisierung' },
+    'retrieval': { icon: '🔍', displayName: 'Datensuche' },
+    'selection': { icon: '🎯', displayName: 'Tool-Auswahl' },
+    'micro-task': { icon: '⚡', displayName: 'Detail-Analyse' },
+    'validation': { icon: '✓', displayName: 'Validierung' },
+    'completion': { icon: '✅', displayName: 'Finalisierung' }
+  },
+
+  actionTranslations: {
+    'pipeline-start': 'Analyse gestartet',
+    'embeddings-search': 'Ähnliche Tools gesucht', 
+    'ai-tool-selection': 'Tools automatisch ausgewählt',
+    'ai-analysis': 'KI-Analyse durchgeführt',
+    'phase-tool-selection': 'Phasen-Tools evaluiert',
+    'tool-evaluation': 'Tool-Bewertung erstellt',
+    'background-knowledge-selection': 'Hintergrundwissen ausgewählt',
+    'confidence-scoring': 'Vertrauenswertung berechnet',
+    'pipeline-end': 'Analyse abgeschlossen'
+  },
+
+  getActionDisplayName(action) {
+    return this.actionTranslations[action] || action;
+  },
+
+  formatDuration(ms) {
+    if (ms < 1000) return '< 1s';
+    if (ms < 60000) return `${Math.ceil(ms / 1000)}s`;
+    const minutes = Math.floor(ms / 60000);
+    const seconds = Math.ceil((ms % 60000) / 1000);
+    return seconds > 0 ? `${minutes}m ${seconds}s` : `${minutes}m`;
+  },
+
+  getConfidenceColor(confidence) {
+    if (confidence >= 80) return 'var(--color-accent)';
+    if (confidence >= 60) return 'var(--color-warning)';
+    return 'var(--color-error)';
+  },
+
+  escapeHtml(text) {
+    if (typeof text !== 'string') return String(text);
+    const div = document.createElement('div');
+    div.textContent = text;
+    return div.innerHTML;
+  },
+
+  truncateText(text, maxLength) {
+    if (!text || text.length <= maxLength) return text;
+    return text.slice(0, maxLength) + '...';
+  },
+
+  sanitizeText(text) {
+    if (typeof text !== 'string') return '';
+    
+    return text
+      .replace(/^#{1,6}\s+/gm, '')
+      .replace(/^\s*[-*+]\s+/gm, '')
+      .replace(/^\s*\d+\.\s+/gm, '')
+      .replace(/\*\*(.+?)\*\*/g, '$1')
+      .replace(/\*(.+?)\*/g, '$1')
+      .replace(/\[([^\]]+)\]\([^)]+\)/g, '$1')
+      .replace(/```[\s\S]*?```/g, '[CODE BLOCK]')
+      .replace(/`([^`]+)`/g, '$1')
+      .replace(/<[^>]+>/g, '')
+      .replace(/\n\s*\n\s*\n/g, '\n\n')
+      .trim();
+  },
+
+  summarizeData(data) {
+    if (data === null || data === undefined) return 'null';
+    if (typeof data === 'string') {
+      return data.length > 100 ? data.slice(0, 100) + '...' : data;
+    }
+    if (typeof data === 'number' || typeof data === 'boolean') {
+      return data.toString();
+    }
+    if (Array.isArray(data)) {
+      if (data.length === 0) return '[]';
+      if (data.length <= 3) return JSON.stringify(data);
+      return `[${data.slice(0, 3).map(i => typeof i === 'string' ? i : JSON.stringify(i)).join(', ')}, ...+${data.length - 3}]`;
+    }
+    if (typeof data === 'object') {
+      const keys = Object.keys(data);
+      if (keys.length === 0) return '{}';
+      if (keys.length <= 3) {
+        return '{' + keys.map(k => `${k}: ${typeof data[k] === 'string' ? data[k].slice(0, 20) + (data[k].length > 20 ? '...' : '') : JSON.stringify(data[k])}`).join(', ') + '}';
+      }
+      return `{${keys.slice(0, 3).join(', ')}, ...+${keys.length - 3} keys}`;
+    }
+    return String(data);
+  },
+
+  showElement(element) {
+    if (element) {
+      element.style.display = 'block';
+      element.classList.remove('hidden');
+    }
+  },
+
+  hideElement(element) {
+    if (element) {
+      element.style.display = 'none';
+      element.classList.add('hidden');
+    }
+  }
+};
+
+class AuditTrailProcessor {
+  static processAuditTrail(rawAuditTrail) {
+    if (!rawAuditTrail || !Array.isArray(rawAuditTrail) || rawAuditTrail.length === 0) {
+      console.log('[AUDIT] No audit trail data provided');
+      return null;
+    }
+
+    try {
+      console.log('[AUDIT] Processing', rawAuditTrail.length, 'audit entries');
+
+      const totalTime = rawAuditTrail.reduce((sum, entry) => sum + (entry.processingTimeMs || 0), 0);
+      const validConfidenceEntries = rawAuditTrail.filter(entry => typeof entry.confidence === 'number');
+      const avgConfidence = validConfidenceEntries.length > 0 
+        ? Math.round(validConfidenceEntries.reduce((sum, entry) => sum + entry.confidence, 0) / validConfidenceEntries.length)
+        : 0;
+      
+      const highConfidenceSteps = rawAuditTrail.filter(entry => (entry.confidence || 0) >= 80).length;
+      const lowConfidenceSteps = rawAuditTrail.filter(entry => (entry.confidence || 0) < 60).length;
+
+      const groupedEntries = rawAuditTrail.reduce((groups, entry) => {
+        const phase = entry.phase || 'unknown';
+        if (!groups[phase]) groups[phase] = [];
+        groups[phase].push(entry);
+        return groups;
+      }, {});
+
+      const phases = Object.entries(groupedEntries).map(([phase, entries]) => {
+        const phaseConfig = Utils.phaseConfig[phase] || { icon: '📋', displayName: phase };
+        const validEntries = entries.filter(entry => entry && typeof entry === 'object');
+        
+        const phaseAvgConfidence = validEntries.length > 0 
+          ? Math.round(validEntries.reduce((sum, entry) => sum + (entry.confidence || 0), 0) / validEntries.length)
+          : 0;
+        
+        const phaseTotalTime = validEntries.reduce((sum, entry) => sum + (entry.processingTimeMs || 0), 0);
+
+        return {
+          name: phase,
+          icon: phaseConfig.icon,
+          displayName: phaseConfig.displayName,
+          avgConfidence: phaseAvgConfidence,
+          totalTime: phaseTotalTime,
+          entries: validEntries.map(e => this.compressEntry(e)).filter(e => e !== null)
+        };
+      }).filter(phase => phase.entries.length > 0);
+
+      return {
+        totalTime,
+        avgConfidence,
+        stepCount: rawAuditTrail.length,
+        highConfidenceSteps,
+        lowConfidenceSteps,
+        phases,
+        summary: this.generateSummary(rawAuditTrail, avgConfidence, lowConfidenceSteps)
+      };
+      
+    } catch (error) {
+      console.error('[AUDIT] Error processing audit trail:', error);
+      return null;
+    }
+  }
+
+  static compressEntry(entry) {
+    if (!entry || typeof entry !== 'object') {
+      console.warn('[AUDIT] Invalid audit entry:', entry);
+      return null;
+    }
+
+    try {
+      return {
+        timestamp: entry.timestamp || Date.now(),
+        phase: entry.phase || 'unknown',
+        action: entry.action || 'unknown',
+        inputSummary: Utils.summarizeData(entry.input),
+        outputSummary: Utils.summarizeData(entry.output),
+        confidence: entry.confidence || 0,
+        processingTimeMs: entry.processingTimeMs || 0,
+        metadata: entry.metadata || {}
+      };
+    } catch (error) {
+      console.error('[AUDIT] Error compressing entry:', error);
+      return null;
+    }
+  }
+
+  static generateSummary(entries, avgConfidence, lowConfidenceSteps) {
+    let analysisQuality;
+    if (avgConfidence >= 85 && lowConfidenceSteps === 0) {
+      analysisQuality = 'excellent';
+    } else if (avgConfidence >= 70 && lowConfidenceSteps <= 1) {
+      analysisQuality = 'good';
+    } else if (avgConfidence >= 60 && lowConfidenceSteps <= 3) {
+      analysisQuality = 'fair';
+    } else {
+      analysisQuality = 'poor';
+    }
+
+    const keyInsights = [];
+    const embeddingsUsed = entries.some(e => e.action === 'embeddings-search');
+    if (embeddingsUsed) {
+      keyInsights.push('Semantische Suche wurde erfolgreich eingesetzt');
+    }
+
+    const toolSelectionEntries = entries.filter(e => e.action === 'ai-tool-selection');
+    if (toolSelectionEntries.length > 0) {
+      const avgSelectionConfidence = toolSelectionEntries.reduce((sum, e) => sum + e.confidence, 0) / toolSelectionEntries.length;
+      if (avgSelectionConfidence >= 80) {
+        keyInsights.push('Hohe Konfidenz bei der Tool-Auswahl');
+      }
+    }
+
+    const potentialIssues = [];
+    if (lowConfidenceSteps > 2) {
+      potentialIssues.push(`${lowConfidenceSteps} Analyseschritte mit niedriger Konfidenz`);
+    }
+
+    const longSteps = entries.filter(e => e.processingTimeMs > 5000);
+    if (longSteps.length > 0) {
+      potentialIssues.push(`${longSteps.length} Schritte benötigten mehr als 5 Sekunden`);
+    }
+
+    return { analysisQuality, keyInsights, potentialIssues };
+  }
+}
+
+class AuditTrailRenderer {
+  constructor(containerId, options = {}) {
+    this.containerId = containerId;
+    this.options = {
+      title: options.title || 'KI-Entscheidungspfad',
+      collapsible: options.collapsible !== false,
+      defaultExpanded: options.defaultExpanded || false,
+      ...options
+    };
+    this.componentId = `audit-trail-${Date.now()}-${Math.random().toString(36).substr(2, 6)}`;
+  }
+
+  render(rawAuditTrail) {
+    const container = document.getElementById(this.containerId);
+    if (!container) {
+      console.error(`[AUDIT RENDERER] Container ${this.containerId} not found`);
+      return;
+    }
+
+    if (!rawAuditTrail || !Array.isArray(rawAuditTrail) || rawAuditTrail.length === 0) {
+      this.renderEmpty(container);
+      return;
+    }
+
+    try {
+      const processedAudit = AuditTrailProcessor.processAuditTrail(rawAuditTrail);
+      
+      if (processedAudit && processedAudit.phases && processedAudit.phases.length > 0) {
+        this.renderProcessed(container, processedAudit);
+        setTimeout(() => this.attachEventHandlers(), 0);
+      } else {
+        this.renderEmpty(container);
+      }
+    } catch (error) {
+      console.error('[AUDIT RENDERER] Failed to render audit trail:', error);
+      this.renderError(container, error);
+    }
+  }
+
+  renderProcessed(container, processedAudit) {
+    const detailsId = `${this.componentId}-details`;
+    
+    container.innerHTML = `
+      <div class="audit-trail-container">
+        <div class="audit-trail-header ${this.options.collapsible ? 'clickable' : ''}" 
+             ${this.options.collapsible ? `data-target="${detailsId}"` : ''}>
+          <div class="audit-trail-title">
+            <div class="audit-icon">
+              <div class="audit-icon-gradient">✓</div>
+              <h4>${this.options.title}</h4>
+            </div>
+            
+            <div class="audit-stats">
+              <div class="stat-item">
+                <div class="stat-dot stat-time"></div>
+                <span>${Utils.formatDuration(processedAudit.totalTime)}</span>
+              </div>
+              <div class="stat-item">
+                <div class="stat-dot" style="background-color: ${Utils.getConfidenceColor(processedAudit.avgConfidence)}"></div>
+                <span>${processedAudit.avgConfidence}% Vertrauen</span>
+              </div>
+              <div class="stat-item">
+                <span>${processedAudit.stepCount} Schritte</span>
+              </div>
+            </div>
+          </div>
+          
+          ${this.options.collapsible ? `
+            <div class="toggle-icon">
+              <svg width="16" height="16" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2">
+                <polyline points="6 9 12 15 18 9"/>
+              </svg>
+            </div>
+          ` : ''}
+        </div>
+        
+        <div id="${detailsId}" class="audit-trail-details ${this.options.collapsible && !this.options.defaultExpanded ? 'collapsed' : ''}">
+          ${this.renderSummary(processedAudit)}
+          ${this.renderProcessFlow(processedAudit)}
+          ${this.renderTechnicalDetails(processedAudit)}
+        </div>
+      </div>
+    `;
+  }
+
+  renderSummary(audit) {
+    return `
+      <div class="audit-summary">
+        <div class="summary-header">📊 Analyse-Qualität</div>
+        <div class="summary-grid">
+          <div class="summary-stat">
+            <div class="summary-value success">${audit.highConfidenceSteps}</div>
+            <div class="summary-label">Hohe Sicherheit</div>
+          </div>
+          <div class="summary-stat">
+            <div class="summary-value ${audit.lowConfidenceSteps > 0 ? 'warning' : 'success'}">
+              ${audit.lowConfidenceSteps}
+            </div>
+            <div class="summary-label">Unsichere Schritte</div>
+          </div>
+          <div class="summary-stat">
+            <div class="summary-value">${Utils.formatDuration(audit.totalTime)}</div>
+            <div class="summary-label">Verarbeitungszeit</div>
+          </div>
+        </div>
+        
+        ${audit.summary.keyInsights && audit.summary.keyInsights.length > 0 ? `
+          <div class="insights-section">
+            <div class="insights-header success">✓ Erkenntnisse:</div>
+            <ul class="insights-list">
+              ${audit.summary.keyInsights.map(insight => `<li>${Utils.escapeHtml(insight)}</li>`).join('')}
+            </ul>
+          </div>
+        ` : ''}
+        
+        ${audit.summary.potentialIssues && audit.summary.potentialIssues.length > 0 ? `
+          <div class="insights-section">
+            <div class="insights-header warning">⚠ Hinweise:</div>
+            <ul class="insights-list">
+              ${audit.summary.potentialIssues.map(issue => `<li>${Utils.escapeHtml(issue)}</li>`).join('')}
+            </ul>
+          </div>
+        ` : ''}
+      </div>
+    `;
+  }
+
+  renderProcessFlow(audit) {
+    if (!audit.phases || audit.phases.length === 0) {
+      return '<div class="audit-process-flow"><p>Keine Phasen verfügbar</p></div>';
+    }
+
+    return `
+      <div class="audit-process-flow">
+        ${audit.phases.map((phase, index) => `
+          <div class="phase-group ${index === audit.phases.length - 1 ? 'last-phase' : ''}">
+            <div class="phase-header" style="display: flex; align-items: center; gap: 1rem; margin-bottom: 0.75rem;">
+              <div class="phase-info" style="display: flex; align-items: center; gap: 0.75rem;">
+                <span class="phase-icon" style="font-size: 1rem;">${phase.icon || '📋'}</span>
+                <span class="phase-name" style="font-size: 0.875rem; font-weight: 500; color: var(--color-text);">${phase.displayName || phase.name}</span>
+              </div>
+              <div class="phase-divider" style="flex: 1; height: 1px; background-color: var(--color-border);"></div>
+              <div class="phase-stats" style="display: flex; align-items: center; gap: 0.5rem; flex-shrink: 0;">
+                <div class="confidence-bar" style="width: 48px; height: 8px; background-color: var(--color-bg-tertiary); border-radius: 4px; overflow: hidden;">
+                  <div class="confidence-fill" 
+                      style="height: 100%; width: ${phase.avgConfidence || 0}%; background-color: ${Utils.getConfidenceColor(phase.avgConfidence || 0)}; border-radius: 4px; transition: var(--transition-fast);">
+                  </div>
+                </div>
+                <span class="confidence-text" style="font-size: 0.75rem; color: var(--color-text-secondary); min-width: 28px;">${phase.avgConfidence || 0}%</span>
+              </div>
+            </div>
+            
+            <div class="phase-entries" style="margin-left: 1.5rem; display: grid; gap: 0.5rem;">
+              ${(phase.entries || []).map(entry => this.renderAuditEntry(entry)).join('')}
+            </div>
+          </div>
+        `).join('')}
+      </div>
+    `;
+  }
+
+  renderAuditEntry(entry) {
+    return `
+      <div class="audit-entry" style="background-color: var(--color-bg); border: 1px solid var(--color-border); border-radius: 0.375rem; padding: 0.75rem; transition: var(--transition-fast);" 
+           onmouseover="this.style.backgroundColor='var(--color-bg-secondary)'" 
+           onmouseout="this.style.backgroundColor='var(--color-bg)'">
+        <div class="entry-main" style="display: flex; align-items: center; justify-content: space-between; margin-bottom: 0.5rem;">
+          <span class="entry-action" style="font-size: 0.875rem; font-weight: 500; color: var(--color-text);">
+            ${Utils.getActionDisplayName(entry.action)}
+          </span>
+          <div class="entry-meta" style="display: flex; align-items: center; gap: 0.5rem; font-size: 0.75rem; color: var(--color-text-secondary);">
+            <div class="confidence-indicator" 
+                style="width: 8px; height: 8px; border-radius: 50%; background-color: ${Utils.getConfidenceColor(entry.confidence || 0)};">
+            </div>
+            <span class="confidence-value" style="min-width: 28px; text-align: right;">${entry.confidence || 0}%</span>
+            <span class="processing-time" style="min-width: 40px; text-align: right;">${entry.processingTimeMs || 0}ms</span>
+          </div>
+        </div>
+        
+        ${(entry.inputSummary && entry.inputSummary !== 'null') || (entry.outputSummary && entry.outputSummary !== 'null') ? `
+          <div class="entry-details" style="font-size: 0.75rem; color: var(--color-text-secondary); padding-top: 0.5rem; border-top: 1px solid var(--color-border);">
+            ${entry.inputSummary && entry.inputSummary !== 'null' ? `
+              <div class="detail-item" style="margin-bottom: 0.25rem; word-break: break-word;">
+                <strong>Input:</strong> ${Utils.escapeHtml(entry.inputSummary)}
+              </div>
+            ` : ''}
+            ${entry.outputSummary && entry.outputSummary !== 'null' ? `
+              <div class="detail-item" style="margin-bottom: 0.25rem; word-break: break-word;">
+                <strong>Output:</strong> ${Utils.escapeHtml(entry.outputSummary)}
+              </div>
+            ` : ''}
+          </div>
+        ` : ''}
+      </div>
+    `;
+  }
+
+  renderTechnicalDetails(audit) {
+    const technicalId = `${this.componentId}-technical`;
+    
+    return `
+      <div class="technical-toggle">
+        <button class="technical-toggle-btn" data-target="${technicalId}">
+          🔧 Technische Details anzeigen
+        </button>
+        <div id="${technicalId}" class="technical-details collapsed">
+          ${(audit.phases || []).map(phase => 
+            (phase.entries || []).map(entry => `
+              <div class="technical-entry">
+                <div class="technical-header">
+                  <span class="technical-phase">${entry.phase}/${entry.action}</span>
+                  <span class="technical-time">
+                    ${new Date(entry.timestamp).toLocaleTimeString('de-DE', { 
+                      hour: '2-digit', 
+                      minute: '2-digit', 
+                      second: '2-digit' 
+                    })} • ${entry.processingTimeMs || 0}ms
+                  </span>
+                </div>
+                <div class="technical-content">
+                  <div class="technical-row">
+                    <strong>Confidence:</strong> ${entry.confidence || 0}%
+                  </div>
+                  ${entry.metadata && Object.keys(entry.metadata).length > 0 ? `
+                    <div class="technical-row">
+                      <strong>Metadata:</strong> ${Utils.escapeHtml(JSON.stringify(entry.metadata))}
+                    </div>
+                  ` : ''}
+                </div>
+              </div>
+            `).join('')
+          ).join('')}
+        </div>
+      </div>
+    `;
+  }
+
+  renderEmpty(container) {
+    container.innerHTML = `
+      <div class="audit-trail-container">
+        <div class="audit-trail-header">
+          <div class="audit-trail-title">
+            <div class="audit-icon">
+              <div class="audit-icon-gradient">ⓘ</div>
+              <h4>Kein Audit-Trail verfügbar</h4>
+            </div>
+          </div>
+        </div>
+      </div>
+    `;
+  }
+
+  renderError(container, error) {
+    container.innerHTML = `
+      <div class="audit-trail-container">
+        <div class="audit-trail-header">
+          <div class="audit-trail-title">
+            <div class="audit-icon">
+              <div class="audit-icon-gradient" style="background: var(--color-error);">✗</div>
+              <h4>Audit-Trail Fehler</h4>
+            </div>
+          </div>
+        </div>
+        <div class="audit-summary">
+          <p style="color: var(--color-error);">
+            Fehler beim Laden der Audit-Informationen: ${Utils.escapeHtml(error.message)}
+          </p>
+        </div>
+      </div>
+    `;
+  }
+
+  attachEventHandlers() {
+    if (this.options.collapsible) {
+      const header = document.querySelector(`[data-target="${this.componentId}-details"]`);
+      const details = document.getElementById(`${this.componentId}-details`);
+      const toggleIcon = header?.querySelector('.toggle-icon svg');
+      
+      if (header && details && toggleIcon) {
+        header.addEventListener('click', () => {
+          const isCollapsed = details.classList.contains('collapsed');
+          details.classList.toggle('collapsed');
+          toggleIcon.style.transform = isCollapsed ? 'rotate(180deg)' : 'rotate(0deg)';
+        });
+      }
+    }
+    
+    const technicalBtn = document.querySelector(`[data-target="${this.componentId}-technical"]`);
+    const technicalDetails = document.getElementById(`${this.componentId}-technical`);
+    
+    if (technicalBtn && technicalDetails) {
+      technicalBtn.addEventListener('click', () => {
+        const isCollapsed = technicalDetails.classList.contains('collapsed');
+        technicalDetails.classList.toggle('collapsed');
+        technicalBtn.textContent = isCollapsed ? 
+          '🔧 Technische Details ausblenden' : 
+          '🔧 Technische Details anzeigen';
+      });
+    }
+  }
+}
 
 class AIQueryInterface {
   constructor() {
@@ -264,11 +802,7 @@ class AIQueryInterface {
       microTaskCounter: document.getElementById('micro-task-counter')
     };
 
-    if (!this.elements.input || !this.elements.submitBtn || !this.elements.results) {
-      console.error('[AI Interface] Critical elements not found');
-      return false;
-    }
-    return true;
+    return Object.values(this.elements).some(el => el !== null);
   }
 
   setupEventListeners() {
@@ -285,7 +819,6 @@ class AIQueryInterface {
     });
 
     this.elements.submitBtn?.addEventListener('click', () => this.handleSubmit());
-
     this.elements.dismissSuggestions?.addEventListener('click', () => this.hideSmartPrompting());
   }
 
@@ -327,28 +860,24 @@ class AIQueryInterface {
     if (this.elements.submitBtnText) this.elements.submitBtnText.textContent = config.submitText;
     if (this.elements.loadingText) this.elements.loadingText.textContent = config.loadingText;
     
-    if (this.currentMode === 'workflow') {
-      if (this.elements.toggleSlider) this.elements.toggleSlider.style.transform = 'translateX(0)';
-      if (this.elements.toggleSwitch) this.elements.toggleSwitch.style.backgroundColor = 'var(--color-primary)';
-      if (this.elements.workflowLabel) {
-        this.elements.workflowLabel.style.color = 'var(--color-primary)';
-        this.elements.workflowLabel.classList.add('active');
-      }
-      if (this.elements.toolLabel) {
-        this.elements.toolLabel.style.color = 'var(--color-text-secondary)';
-        this.elements.toolLabel.classList.remove('active');
-      }
-    } else {
-      if (this.elements.toggleSlider) this.elements.toggleSlider.style.transform = 'translateX(26px)';
-      if (this.elements.toggleSwitch) this.elements.toggleSwitch.style.backgroundColor = 'var(--color-accent)';
-      if (this.elements.toolLabel) {
-        this.elements.toolLabel.style.color = 'var(--color-accent)';
-        this.elements.toolLabel.classList.add('active');
-      }
-      if (this.elements.workflowLabel) {
-        this.elements.workflowLabel.style.color = 'var(--color-text-secondary)';
-        this.elements.workflowLabel.classList.remove('active');
-      }
+    const isWorkflow = this.currentMode === 'workflow';
+    const primaryColor = isWorkflow ? 'var(--color-primary)' : 'var(--color-accent)';
+    const secondaryColor = 'var(--color-text-secondary)';
+    
+    if (this.elements.toggleSlider) {
+      this.elements.toggleSlider.style.transform = isWorkflow ? 'translateX(0)' : 'translateX(26px)';
+    }
+    if (this.elements.toggleSwitch) {
+      this.elements.toggleSwitch.style.backgroundColor = primaryColor;
+    }
+    
+    if (this.elements.workflowLabel) {
+      this.elements.workflowLabel.style.color = isWorkflow ? primaryColor : secondaryColor;
+      this.elements.workflowLabel.classList.toggle('active', isWorkflow);
+    }
+    if (this.elements.toolLabel) {
+      this.elements.toolLabel.style.color = isWorkflow ? secondaryColor : primaryColor;
+      this.elements.toolLabel.classList.toggle('active', !isWorkflow);
     }
   }
 
@@ -432,44 +961,65 @@ class AIQueryInterface {
     const statusText = this.elements.promptingStatus.querySelector('.status-text');
     if (!statusText) return;
     
-    switch (state) {
-      case 'analyzing':
-        this.showElement(this.elements.promptingContainer);
-        this.hideElement(this.elements.smartHint);
-        statusText.textContent = 'Analysiere Eingabe...';
-        this.showElement(this.elements.promptingSpinner);
-        this.hideElement(this.elements.suggestedQuestions);
-        break;
-      case 'suggestions':
-        statusText.textContent = 'Verbesserungsvorschläge verfügbar';
-        this.hideElement(this.elements.promptingSpinner);
-        this.showElement(this.elements.suggestedQuestions);
-        break;
-      case 'rate-limited':
-        statusText.textContent = 'Nach Hauptabfrage verfügbar';
-        this.hideElement(this.elements.promptingSpinner);
-        this.hideElement(this.elements.suggestedQuestions);
-        break;
-      case 'error':
-      case 'hidden':
-        this.hideSmartPrompting();
-        break;
-    }
+    const statusMap = {
+      analyzing: {
+        text: 'Analysiere Eingabe...',
+        showSpinner: true,
+        showSuggestions: false,
+        showContainer: true,
+        hideHint: true
+      },
+      suggestions: {
+        text: 'Verbesserungsvorschläge verfügbar',
+        showSpinner: false,
+        showSuggestions: true,
+        showContainer: true,
+        hideHint: true
+      },
+      'rate-limited': {
+        text: 'Nach Hauptabfrage verfügbar',
+        showSpinner: false,
+        showSuggestions: false,
+        showContainer: true,
+        hideHint: true
+      },
+      error: {
+        showContainer: false,
+        hideHint: false
+      }
+    };
+
+    const config = statusMap[state];
+    if (!config) return;
+
+    statusText.textContent = config.text || '';
+    
+    if (config.showContainer) Utils.showElement(this.elements.promptingContainer);
+    else Utils.hideElement(this.elements.promptingContainer);
+    
+    if (config.hideHint) Utils.hideElement(this.elements.smartHint);
+    else Utils.showElement(this.elements.smartHint);
+    
+    if (config.showSpinner) Utils.showElement(this.elements.promptingSpinner);
+    else Utils.hideElement(this.elements.promptingSpinner);
+    
+    if (config.showSuggestions) Utils.showElement(this.elements.suggestedQuestions);
+    else Utils.hideElement(this.elements.suggestedQuestions);
   }
 
   displaySuggestions(suggestions) {
     if (!this.elements.questionsList || !suggestions?.length) return;
     
     this.elements.questionsList.innerHTML = suggestions.map(question => 
-      `<div class="suggestion-item">${this.escapeHtml(question)}</div>`
+      `<div class="suggestion-item">${Utils.escapeHtml(question)}</div>`
     ).join('');
     
     this.showPromptingStatus('suggestions');
   }
 
   hideSmartPrompting() {
-    if (this.elements.promptingContainer) this.hideElement(this.elements.promptingContainer);
-    if (this.elements.smartHint) this.showElement(this.elements.smartHint);
+    if (this.elements.promptingContainer) Utils.hideElement(this.elements.promptingContainer);
+    if (this.elements.smartHint) Utils.showElement(this.elements.smartHint);
   }
 
   async handleSubmit() {
@@ -519,7 +1069,7 @@ class AIQueryInterface {
       }
 
       this.currentRecommendation = data.recommendation;
-      this.displayResults(data.recommendation, query);
+      await this.displayResults(data.recommendation, query);
       
     } catch (error) {
       console.error('[AI Interface] Request failed:', error);
@@ -532,15 +1082,17 @@ class AIQueryInterface {
   }
 
   getErrorMessage(error) {
-    if (error.message.includes('429')) {
-      return 'Zu viele Anfragen. Bitte warten Sie einen Moment.';
-    } else if (error.message.includes('401')) {
-      return 'Authentifizierung erforderlich. Bitte melden Sie sich an.';
-    } else if (error.message.includes('503')) {
-      return 'KI-Service vorübergehend nicht verfügbar.';
-    } else {
-      return `Fehler: ${error.message}`;
+    const errorMap = {
+      '429': 'Zu viele Anfragen. Bitte warten Sie einen Moment.',
+      '401': 'Authentifizierung erforderlich. Bitte melden Sie sich an.',
+      '503': 'KI-Service vorübergehend nicht verfügbar.'
+    };
+    
+    for (const [code, message] of Object.entries(errorMap)) {
+      if (error.message.includes(code)) return message;
     }
+    
+    return `Fehler: ${error.message}`;
   }
 
   resetSubmitButton() {
@@ -552,10 +1104,7 @@ class AIQueryInterface {
   }
 
   startQueueMonitoring(taskId) {
-    if (this.elements.queueStatus) {
-      this.showElement(this.elements.queueStatus);
-    }
-    
+    if (this.elements.queueStatus) Utils.showElement(this.elements.queueStatus);
     if (this.elements.taskIdDisplay) {
       this.elements.taskIdDisplay.textContent = taskId.slice(-8);
     }
@@ -578,7 +1127,7 @@ class AIQueryInterface {
       if (this.elements.queueLength) this.elements.queueLength.textContent = data.queueLength || 0;
       if (this.elements.estimatedTime) {
         this.elements.estimatedTime.textContent = data.estimatedWaitTime > 0 ? 
-          this.formatDuration(data.estimatedWaitTime) : 'Verarbeitung läuft...';
+          Utils.formatDuration(data.estimatedWaitTime) : 'Verarbeitung läuft...';
       }
       
       if (this.elements.positionBadge) {
@@ -589,11 +1138,7 @@ class AIQueryInterface {
             this.elements.progressBar.style.width = `${progress}%`;
           }
         } else {
-          const stateIcons = {
-            processing: '⚡',
-            completed: '✅', 
-            failed: '❌'
-          };
+          const stateIcons = { processing: '⚡', completed: '✅', failed: '❌' };
           this.elements.positionBadge.textContent = stateIcons[data.taskStatus] || '?';
           if (this.elements.progressBar) {
             this.elements.progressBar.style.width = data.taskStatus === 'processing' ? '100%' : '0%';
@@ -617,14 +1162,14 @@ class AIQueryInterface {
       this.microTaskInterval = null;
     }
     
-    if (this.elements.queueStatus) this.hideElement(this.elements.queueStatus);
-    if (this.elements.microTaskProgress) this.hideElement(this.elements.microTaskProgress);
+    if (this.elements.queueStatus) Utils.hideElement(this.elements.queueStatus);
+    if (this.elements.microTaskProgress) Utils.hideElement(this.elements.microTaskProgress);
   }
 
   startMicroTaskProgress() {
     if (!this.elements.microTaskProgress) return;
     
-    this.showElement(this.elements.microTaskProgress);
+    Utils.showElement(this.elements.microTaskProgress);
     this.currentMicroTaskStep = 0;
     
     const steps = ['scenario', 'approach', 'considerations', 'tools', 'knowledge', 'final'];
@@ -671,26 +1216,27 @@ class AIQueryInterface {
     }, 2000);
   }
 
-  displayResults(recommendation, originalQuery) {
-    console.log('[AI DEBUG] Full recommendation object:', recommendation);
-    console.log('[AI DEBUG] Recommended tools:', recommendation.recommended_tools);
-    
-    if (recommendation.recommended_tools) {
-      recommendation.recommended_tools.forEach((tool, index) => {
-        console.log(`[AI DEBUG] Tool ${index}:`, tool.name, 'Confidence:', tool.confidence);
-      });
-    }
+  async displayResults(recommendation, originalQuery) {
+    console.log('[AI Interface] Displaying results for mode:', this.currentMode);
     
     if (this.currentMode === 'workflow') {
-      this.displayWorkflowResults(recommendation, originalQuery);
+      await this.displayWorkflowResults(recommendation, originalQuery);
     } else {
-      this.displayToolResults(recommendation, originalQuery);
+      await this.displayToolResults(recommendation, originalQuery);
     }
     
     this.showResults();
+    
+    setTimeout(() => {
+      try {
+        this.renderAuditTrail(recommendation.auditTrail);
+      } catch (error) {
+        console.error('[AI Interface] Failed to render audit trail:', error);
+      }
+    }, 100);
   }
 
-  displayWorkflowResults(recommendation, originalQuery) {
+  async displayWorkflowResults(recommendation, originalQuery) {
     const toolsByPhase = {};
     const phaseOrder = phases.map(phase => phase.id);
     const phaseNames = phases.reduce((acc, phase) => {
@@ -702,12 +1248,7 @@ class AIQueryInterface {
       toolsByPhase[phase] = [];
     });
 
-    console.log('[AI Results] Recommendation structure:', recommendation);
-    console.log('[AI Results] Recommended tools:', recommendation.recommended_tools);
-
     recommendation.recommended_tools?.forEach(recTool => {
-      console.log('[AI Results] Tool confidence data:', recTool.name, recTool.confidence);
-      
       if (toolsByPhase[recTool.phase]) {
         const fullTool = tools.find(t => t.name === recTool.name);
         if (fullTool) {
@@ -716,8 +1257,7 @@ class AIQueryInterface {
             recommendation: recTool,
             confidence: recTool.confidence, 
             justification: recTool.justification,
-            priority: recTool.priority,
-            recommendationStrength: recTool.recommendationStrength
+            priority: recTool.priority
           });
         }
       }
@@ -730,14 +1270,14 @@ class AIQueryInterface {
         ${this.renderBackgroundKnowledge(recommendation.background_knowledge)}
         ${this.renderWorkflowPhases(toolsByPhase, phaseOrder, phaseNames)}
         ${this.renderWorkflowSuggestion(recommendation.workflow_suggestion)}
-        ${this.renderAuditTrail(recommendation.auditTrail)}
       </div>
+      <div id="audit-trail-container"></div>
     `;
     
     this.elements.results.innerHTML = html;
   }
 
-  displayToolResults(recommendation, originalQuery) {
+  async displayToolResults(recommendation, originalQuery) {
     const html = `
       <div class="workflow-container">
         ${this.renderHeader('Handlungsempfehlung', originalQuery)}
@@ -745,426 +1285,38 @@ class AIQueryInterface {
         ${this.renderBackgroundKnowledge(recommendation.background_knowledge)}
         ${this.renderToolRecommendations(recommendation.recommended_tools)}
         ${this.renderAdditionalConsiderations(recommendation.additional_considerations)}
-        ${this.renderAuditTrail(recommendation.auditTrail)}
       </div>
+      <div id="audit-trail-container"></div>
     `;
     
     this.elements.results.innerHTML = html;
   }
 
-  renderConfidenceTooltip(confidence) {
-    if (!confidence || typeof confidence.overall !== 'number') {
-      return '';
+  renderAuditTrail(rawAuditTrail) {
+    const container = document.getElementById('audit-trail-container');
+    if (!container) {
+      console.error('[AI Interface] Audit container not found');
+      return;
     }
-    
-    const confidenceColor = confidence.overall >= 80 ? 'var(--color-accent)' : 
-                          confidence.overall >= 60 ? 'var(--color-warning)' : 'var(--color-error)';
-    
-    const tooltipId = `tooltip-${Math.random().toString(36).substr(2, 9)}`;
-    
-    return `
-      <span class="confidence-tooltip-trigger" 
-            style="display: inline-flex; align-items: center; gap: 0.125rem; cursor: help; margin-left: 0.25rem;"
-            onmouseenter="document.getElementById('${tooltipId}').style.display = 'block'"
-            onmouseleave="document.getElementById('${tooltipId}').style.display = 'none'"
-            onclick="event.stopPropagation();">
-        <div style="width: 6px; height: 6px; border-radius: 50%; background-color: ${confidenceColor}; flex-shrink: 0;"></div>
-        <span style="font-size: 0.625rem; color: white; font-weight: 600; text-shadow: 0 1px 2px rgba(0,0,0,0.5);">${confidence.overall}%</span>
-        
-        <div id="${tooltipId}" style="display: none; position: fixed; top: 50%; left: 50%; transform: translate(-50%, -50%); z-index: 1001; background: var(--color-bg); border: 1px solid var(--color-border); border-radius: 0.5rem; padding: 1rem; min-width: 320px; max-width: 400px; box-shadow: var(--shadow-lg); font-size: 0.75rem; color: var(--color-text);">
-          <div style="display: flex; justify-content: space-between; align-items: center; margin-bottom: 0.75rem;">
-            <strong style="font-size: 0.875rem;">KI-Vertrauenswertung</strong>
-            <span class="badge badge-mini" style="background-color: ${confidenceColor}; color: white; font-weight: 600;">${confidence.overall}%</span>
-          </div>
-          
-          <div style="display: grid; grid-template-columns: 1fr; gap: 0.625rem; margin-bottom: 0.75rem;">
-            <div style="background: var(--color-bg-secondary); padding: 0.5rem; border-radius: 0.375rem; border-left: 3px solid var(--color-accent);">
-              <div style="display: flex; justify-content: space-between; align-items: center; margin-bottom: 0.25rem;">
-                <span style="font-weight: 600; font-size: 0.6875rem;">🔍 Semantische Relevanz</span>
-                <strong style="color: var(--color-accent);">${confidence.semanticRelevance}%</strong>
-              </div>
-              <div style="font-size: 0.625rem; color: var(--color-text-secondary); line-height: 1.3;">
-                Wie gut die Tool-Beschreibung semantisch zu Ihrer Anfrage passt (Vektor-Ähnlichkeit)
-              </div>
-            </div>
-            
-            <div style="background: var(--color-bg-secondary); padding: 0.5rem; border-radius: 0.375rem; border-left: 3px solid var(--color-primary);">
-              <div style="display: flex; justify-content: space-between; align-items: center; margin-bottom: 0.25rem;">
-                <span style="font-weight: 600; font-size: 0.6875rem;">🎯 Aufgaben-Eignung</span>
-                <strong style="color: var(--color-primary);">${confidence.taskSuitability}%</strong>
-              </div>
-              <div style="font-size: 0.625rem; color: var(--color-text-secondary); line-height: 1.3;">
-                KI-bewertete Eignung des Tools für Ihre spezifische forensische Aufgabenstellung
-              </div>
-            </div>
-          </div>
-          
-          ${confidence.strengthIndicators && confidence.strengthIndicators.length > 0 ? `
-            <div style="margin-bottom: 0.75rem; padding: 0.5rem; background: var(--color-oss-bg); border-radius: 0.375rem; border-left: 3px solid var(--color-accent);">
-              <strong style="color: var(--color-accent); font-size: 0.6875rem; display: flex; align-items: center; gap: 0.25rem; margin-bottom: 0.375rem;">
-                <span>✓</span> Stärken dieser Empfehlung:
-              </strong>
-              <ul style="margin: 0; padding-left: 1rem; font-size: 0.625rem; line-height: 1.4;">
-                ${confidence.strengthIndicators.slice(0, 3).map(s => `<li style="margin-bottom: 0.25rem;">${this.sanitizeText(s)}</li>`).join('')}
-              </ul>
-            </div>
-          ` : ''}
-          
-          ${confidence.uncertaintyFactors && confidence.uncertaintyFactors.length > 0 ? `
-            <div style="padding: 0.5rem; background: var(--color-hosted-bg); border-radius: 0.375rem; border-left: 3px solid var(--color-warning);">
-              <strong style="color: var(--color-warning); font-size: 0.6875rem; display: flex; align-items: center; gap: 0.25rem; margin-bottom: 0.375rem;">
-                <span>⚠</span> Mögliche Einschränkungen:
-              </strong>
-              <ul style="margin: 0; padding-left: 1rem; font-size: 0.625rem; line-height: 1.4;">
-                ${confidence.uncertaintyFactors.slice(0, 3).map(f => `<li style="margin-bottom: 0.25rem;">${this.sanitizeText(f)}</li>`).join('')}
-              </ul>
-            </div>
-          ` : ''}
-          
-          <div style="margin-top: 0.75rem; padding-top: 0.75rem; border-top: 1px solid var(--color-border); font-size: 0.625rem; color: var(--color-text-secondary); text-align: center;">
-            Forensisch fundierte KI-Analyse
-          </div>
-        </div>
-      </span>
-    `;
-  }
 
-  renderAuditTrail(auditTrail) {
-    if (!auditTrail || !Array.isArray(auditTrail) || auditTrail.length === 0) {
-      return '';
+    container.innerHTML = '';
+
+    if (!rawAuditTrail || !Array.isArray(rawAuditTrail) || rawAuditTrail.length === 0) {
+      console.log('[AI Interface] No audit trail data to render');
+      return;
     }
-    
-    const totalTime = auditTrail.reduce((sum, entry) => sum + entry.processingTimeMs, 0);
-    const avgConfidence = auditTrail.reduce((sum, entry) => sum + entry.confidence, 0) / auditTrail.length;
-    const lowConfidenceSteps = auditTrail.filter(entry => entry.confidence < 60).length;
-    const highConfidenceSteps = auditTrail.filter(entry => entry.confidence >= 80).length;
-    
-    const groupedEntries = auditTrail.reduce((groups, entry) => {
-      if (!groups[entry.phase]) groups[entry.phase] = [];
-      groups[entry.phase].push(entry);
-      return groups;
-    }, {});
-    
-    return `
-      <div class="card-info-sm mt-4" style="border-left: 4px solid var(--color-accent);">
-        <div class="flex items-center justify-between mb-3 cursor-pointer" onclick="const container = this.closest('.card-info-sm'); const details = container.querySelector('.audit-trail-details'); const isHidden = details.style.display === 'none'; details.style.display = isHidden ? 'block' : 'none'; this.querySelector('.toggle-icon').style.transform = isHidden ? 'rotate(180deg)' : 'rotate(0deg)';">
-          <div class="flex items-center gap-3">
-            <div class="flex items-center gap-2">
-              <div style="width: 24px; height: 24px; background: linear-gradient(135deg, var(--color-accent) 0%, var(--color-primary) 100%); border-radius: 50%; display: flex; align-items: center; justify-content: center; color: white; font-size: 0.75rem; font-weight: bold;">
-                ✓
-              </div>
-              <h4 class="text-sm font-semibold text-accent mb-0">KI-Entscheidungspfad</h4>
-            </div>
-            
-            <div class="flex gap-3 text-xs">
-              <div class="flex items-center gap-1">
-                <div class="w-2 h-2 rounded-full" style="background-color: var(--color-accent);"></div>
-                <span class="text-muted">${this.formatDuration(totalTime)}</span>
-              </div>
-              <div class="flex items-center gap-1">
-                <div class="w-2 h-2 rounded-full" style="background-color: ${avgConfidence >= 80 ? 'var(--color-accent)' : avgConfidence >= 60 ? 'var(--color-warning)' : 'var(--color-error)'};"></div>
-                <span class="text-muted">${Math.round(avgConfidence)}% Vertrauen</span>
-              </div>
-              <div class="flex items-center gap-1">
-                <span class="text-muted">${auditTrail.length} Schritte</span>
-              </div>
-            </div>
-          </div>
-          
-          <div class="toggle-icon" style="transition: transform 0.2s ease;">
-            <svg width="16" height="16" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2">
-              <polyline points="6 9 12 15 18 9"/>
-            </svg>
-          </div>
-        </div>
-        
-        <div style="display: none;" class="audit-trail-details">
-          <div class="grid gap-4">
-            <!-- Summary Section -->
-            <div class="p-3 rounded-lg" style="background-color: var(--color-bg-tertiary);">
-              <div class="text-xs font-medium mb-2 text-accent">📊 Analyse-Qualität</div>
-              <div class="grid grid-cols-3 gap-3 text-xs">
-                <div class="text-center">
-                  <div class="font-semibold" style="color: var(--color-accent);">${highConfidenceSteps}</div>
-                  <div class="text-muted">Hohe Sicherheit</div>
-                </div>
-                <div class="text-center">
-                  <div class="font-semibold" style="color: ${lowConfidenceSteps > 0 ? 'var(--color-warning)' : 'var(--color-accent)'};">${lowConfidenceSteps}</div>
-                  <div class="text-muted">Unsichere Schritte</div>
-                </div>
-                <div class="text-center">
-                  <div class="font-semibold">${this.formatDuration(totalTime)}</div>
-                  <div class="text-muted">Verarbeitungszeit</div>
-                </div>
-              </div>
-            </div>
-            
-            <!-- Process Flow -->
-            <div class="audit-process-flow">
-              ${Object.entries(groupedEntries).map(([phase, entries]) => this.renderPhaseGroup(phase, entries)).join('')}
-            </div>
-            
-            <!-- Technical Details Toggle -->
-            <div class="text-center">
-              <button class="text-xs text-muted hover:text-primary transition-colors cursor-pointer border-none bg-none" onclick="const techDetails = this.nextElementSibling; const isHidden = techDetails.style.display === 'none'; techDetails.style.display = isHidden ? 'block' : 'none'; this.textContent = isHidden ? '🔧 Technische Details ausblenden' : '🔧 Technische Details anzeigen';">
-                🔧 Technische Details anzeigen
-              </button>
-              <div style="display: none;" class="mt-3 p-3 rounded-lg bg-gray-50 dark:bg-gray-800 text-xs">
-                ${auditTrail.map(entry => this.renderTechnicalEntry(entry)).join('')}
-              </div>
-            </div>
-          </div>
-        </div>
-      </div>
-    `;
-  }
 
-  renderPhaseGroup(phase, entries) {
-    const phaseIcons = {
-      'initialization': '🚀',
-      'retrieval': '🔍',
-      'selection': '🎯',
-      'micro-task': '⚡',
-      'completion': '✅'
-    };
-    
-    const phaseNames = {
-      'initialization': 'Initialisierung',
-      'retrieval': 'Datensuche',
-      'selection': 'Tool-Auswahl',
-      'micro-task': 'Detail-Analyse',
-      'completion': 'Finalisierung'
-    };
-    
-    const avgConfidence = entries.reduce((sum, entry) => sum + entry.confidence, 0) / entries.length;
-    const totalTime = entries.reduce((sum, entry) => sum + entry.processingTimeMs, 0);
-    
-    return `
-      <div class="phase-group mb-4">
-        <div class="flex items-center gap-3 mb-3">
-          <div class="flex items-center gap-2">
-            <span class="text-lg">${phaseIcons[phase] || '📋'}</span>
-            <span class="font-medium text-sm">${phaseNames[phase] || phase}</span>
-          </div>
-          <div class="flex-1 h-px bg-border"></div>
-          <div class="flex items-center gap-2 text-xs text-muted">
-            <div class="confidence-indicator w-12 h-2 rounded-full overflow-hidden" style="background-color: var(--color-bg-tertiary);">
-              <div class="h-full rounded-full transition-all" style="width: ${avgConfidence}%; background-color: ${avgConfidence >= 80 ? 'var(--color-accent)' : avgConfidence >= 60 ? 'var(--color-warning)' : 'var(--color-error)'};"></div>
-            </div>
-            <span>${Math.round(avgConfidence)}%</span>
-          </div>
-        </div>
-        
-        <div class="grid gap-2 ml-6">
-          ${entries.map(entry => this.renderSimplifiedEntry(entry)).join('')}
-        </div>
-      </div>
-    `;
-  }
-
-  renderSimplifiedEntry(entry) {
-    const actionIcons = {
-      'pipeline-start': '▶️',
-      'embeddings-search': '🔍',
-      'ai-tool-selection': '🎯',
-      'ai-analysis': '🧠',
-      'phase-tool-selection': '⚙️',
-      'tool-evaluation': '📊',
-      'background-knowledge-selection': '📚',
-      'pipeline-end': '🏁'
-    };
-    
-    const actionNames = {
-      'pipeline-start': 'Analyse gestartet',
-      'embeddings-search': 'Ähnliche Tools gesucht',
-      'ai-tool-selection': 'Tools automatisch ausgewählt',
-      'ai-analysis': 'KI-Analyse durchgeführt',
-      'phase-tool-selection': 'Phasen-Tools evaluiert',
-      'tool-evaluation': 'Tool-Bewertung erstellt',
-      'background-knowledge-selection': 'Hintergrundwissen ausgewählt',
-      'pipeline-end': 'Analyse abgeschlossen'
-    };
-    
-    const confidenceColor = entry.confidence >= 80 ? 'var(--color-accent)' : 
-                            entry.confidence >= 60 ? 'var(--color-warning)' : 'var(--color-error)';
-    
-    return `
-      <div class="flex items-center gap-3 py-2 px-3 rounded-lg hover:bg-secondary transition-colors">
-        <span class="text-sm">${actionIcons[entry.action] || '📋'}</span>
-        <div class="flex-1 min-w-0">
-          <div class="text-sm font-medium">${actionNames[entry.action] || entry.action}</div>
-          ${entry.output && typeof entry.output === 'object' && entry.output.selectedToolCount ? 
-            `<div class="text-xs text-muted">${entry.output.selectedToolCount} Tools ausgewählt</div>` : ''}
-        </div>
-        <div class="flex items-center gap-2 text-xs">
-          <div class="w-8 h-1.5 rounded-full overflow-hidden" style="background-color: var(--color-bg-tertiary);">
-            <div class="h-full rounded-full" style="width: ${entry.confidence}%; background-color: ${confidenceColor};"></div>
-          </div>
-          <span class="text-muted w-8 text-right">${entry.confidence}%</span>
-          <span class="text-muted w-12 text-right">${entry.processingTimeMs}ms</span>
-        </div>
-      </div>
-    `;
-  }
-
-  renderTechnicalEntry(entry) {
-    const formattedTime = new Date(entry.timestamp).toLocaleTimeString('de-DE', { 
-      hour: '2-digit', 
-      minute: '2-digit', 
-      second: '2-digit' 
-    });
-    
-    return `
-      <div class="border rounded p-2 mb-2" style="border-color: var(--color-border);">
-        <div class="flex justify-between items-center mb-1">
-          <span class="font-mono text-xs">${entry.phase}/${entry.action}</span>
-          <span class="text-xs text-muted">${formattedTime} • ${entry.processingTimeMs}ms</span>
-        </div>
-        ${entry.input && Object.keys(entry.input).length > 0 ? `
-          <div class="text-xs mb-1">
-            <strong>Input:</strong> ${this.formatAuditData(entry.input)}
-          </div>
-        ` : ''}
-        ${entry.output && Object.keys(entry.output).length > 0 ? `
-          <div class="text-xs">
-            <strong>Output:</strong> ${this.formatAuditData(entry.output)}
-          </div>
-        ` : ''}
-      </div>
-    `;
-  }
-
-  renderAuditEntry(entry) {
-    const confidenceColor = entry.confidence >= 80 ? 'var(--color-accent)' : 
-                            entry.confidence >= 60 ? 'var(--color-warning)' : 'var(--color-error)';
-    
-    const formattedTime = new Date(entry.timestamp).toLocaleTimeString('de-DE', { 
-      hour: '2-digit', 
-      minute: '2-digit', 
-      second: '2-digit' 
-    });
-    
-    return `
-      <div class="border-l-2 pl-3 py-2 mb-2" style="border-left-color: ${confidenceColor};">
-        <div class="flex justify-between items-center mb-1">
-          <span class="text-xs font-medium">${entry.phase} → ${entry.action}</span>
-          <div class="flex items-center gap-2">
-            <span class="badge badge-mini" style="background-color: ${confidenceColor}; color: white;">
-              ${entry.confidence}% confidence
-            </span>
-            <span class="text-xs text-muted">${entry.processingTimeMs}ms</span>
-            <span class="text-xs text-muted">${formattedTime}</span>
-          </div>
-        </div>
-        <div class="text-xs text-muted grid-cols-2 gap-2" style="display: grid;">
-          <div><strong>Input:</strong> ${this.formatAuditData(entry.input)}</div>
-          <div><strong>Output:</strong> ${this.formatAuditData(entry.output)}</div>
-        </div>
-        ${entry.metadata && Object.keys(entry.metadata).length > 0 ? `
-          <div class="text-xs text-muted mt-1 pt-1 border-t border-dashed">
-            <strong>Metadata:</strong> ${this.formatAuditData(entry.metadata)}
-          </div>
-        ` : ''}
-      </div>
-    `;
-  }
-
-  formatAuditData(data) {
-    if (data === null || data === undefined) return 'null';
-    if (typeof data === 'string') {
-      return data.length > 100 ? data.slice(0, 100) + '...' : data;
+    try {
+      const renderer = new AuditTrailRenderer('audit-trail-container', {
+        title: 'KI-Entscheidungspfad',
+        collapsible: true,
+        defaultExpanded: false
+      });
+      
+      renderer.render(rawAuditTrail);
+    } catch (error) {
+      console.error('[AI Interface] Failed to render audit trail:', error);
     }
-    if (typeof data === 'number') return data.toString();
-    if (typeof data === 'boolean') return data.toString();
-    if (Array.isArray(data)) {
-      if (data.length === 0) return '[]';
-      if (data.length <= 3) return JSON.stringify(data);
-      return `[${data.slice(0, 3).map(i => typeof i === 'string' ? i : JSON.stringify(i)).join(', ')}, ...+${data.length - 3}]`;
-    }
-    if (typeof data === 'object') {
-      const keys = Object.keys(data);
-      if (keys.length === 0) return '{}';
-      if (keys.length <= 3) {
-        return '{' + keys.map(k => `${k}: ${typeof data[k] === 'string' ? data[k].slice(0, 20) + (data[k].length > 20 ? '...' : '') : JSON.stringify(data[k])}`).join(', ') + '}';
-      }
-      return `{${keys.slice(0, 3).join(', ')}, ...+${keys.length - 3} keys}`;
-    }
-    return String(data);
-  }
-
-  renderWorkflowTool(tool) {
-    const hasValidProjectUrl = isToolHosted(tool);
-    const priorityColors = {
-      high: 'var(--color-error)',
-      medium: 'var(--color-warning)', 
-      low: 'var(--color-accent)'
-    };
-
-    return `
-      <div class="tool-recommendation ${this.getToolClass(tool, 'recommendation')}" onclick="window.showToolDetails('${tool.name}')" style="position: relative;">
-        <div class="tool-rec-header">
-          <h4 class="tool-rec-name">
-            ${tool.icon ? `<span style="margin-right: 0.5rem;">${tool.icon}</span>` : ''}
-            ${tool.name}
-          </h4>
-          <div class="flex items-center gap-2">
-            <span class="tool-rec-priority ${tool.recommendation ? tool.recommendation.priority : tool.priority}" 
-                  style="background-color: ${priorityColors[tool.recommendation ? tool.recommendation.priority : tool.priority]}; color: white; padding: 0.25rem 0.5rem; border-radius: 1rem; font-size: 0.75rem; position: relative;">
-              ${tool.recommendation ? tool.recommendation.priority : tool.priority}
-              ${tool.confidence ? this.renderConfidenceTooltip(tool.confidence, 'priority') : ''}
-            </span>
-          </div>
-        </div>
-        
-        <div class="tool-rec-justification" style="background-color: var(--color-bg-tertiary); padding: 0.75rem; border-radius: 0.375rem; border-left: 3px solid var(--color-primary); margin: 0.75rem 0; font-style: italic; white-space: pre-wrap; word-wrap: break-word;">
-          "${this.sanitizeText(tool.justification || (tool.recommendation && tool.recommendation.justification) || `Empfohlen für ${tool.phase}`)}"
-        </div>
-        
-        <div style="font-size: 0.75rem; color: var(--color-text-secondary);">
-          ${this.renderToolBadges(tool)}
-          <div style="margin-top: 0.5rem;">
-            ${tool.type === 'method' ? 'Methode' : tool.platforms.join(', ') + ' • ' + tool.license}
-          </div>
-        </div>
-      </div>
-    `;
-  }
-
-  renderDetailedTool(tool, recommendation, rank) {
-    const rankColors = { 1: 'var(--color-accent)', 2: 'var(--color-primary)', 3: 'var(--color-warning)' };
-    const suitabilityColors = { high: 'var(--color-accent)', medium: 'var(--color-warning)', low: 'var(--color-text-secondary)' };
-    
-    return `
-      <div class="card ${this.getToolClass(tool, 'card')}" style="cursor: pointer; position: relative;" onclick="window.showToolDetails('${tool.name}')">
-        <div style="position: absolute; top: -8px; right: -8px; width: 32px; height: 32px; background-color: ${rankColors[rank]}; color: white; border-radius: 50%; display: flex; align-items: center; justify-content: center; font-weight: bold; font-size: 1.125rem;">
-          ${rank}
-        </div>
-
-        <div style="margin-bottom: 1rem;">
-          <h3 style="margin: 0 0 0.5rem 0;">${tool.name}</h3>
-          <div style="display: flex; flex-wrap: wrap; gap: 0.5rem; align-items: center; margin-bottom: 0.75rem;">
-            <span class="badge" style="background-color: ${suitabilityColors[recommendation.suitability_score]}; color: white; position: relative;">
-              ${this.getSuitabilityText(recommendation.suitability_score, recommendation.confidence)}
-            </span>
-            ${this.renderToolBadges(tool)}
-          </div>
-        </div>
-
-        <div style="margin-bottom: 1.5rem;">
-          <h4 style="margin: 0.8rem 0 0.75rem 0; color: var(--color-accent);">Warum diese Methode?</h4>
-          <div style="margin: 0; line-height: 1.6; white-space: pre-wrap; word-wrap: break-word;">${this.sanitizeText(recommendation.detailed_explanation)}</div>
-          
-          ${recommendation.implementation_approach ? `
-            <h4 style="margin: 0.8rem 0 0.75rem 0; color: var(--color-primary);">Anwendungsansatz</h4>
-            <div style="margin: 0; line-height: 1.6; white-space: pre-wrap; word-wrap: break-word;">${this.sanitizeText(recommendation.implementation_approach)}</div>
-          ` : ''}
-        </div>
-
-        ${this.renderProsAndCons(recommendation.pros, recommendation.cons)}
-        ${this.renderToolMetadata(tool)}
-        ${recommendation.alternatives ? this.renderAlternatives(recommendation.alternatives) : ''}
-      </div>
-    `;
   }
 
   renderHeader(title, query) {
@@ -1172,7 +1324,7 @@ class AIQueryInterface {
       <div style="text-align: center; margin-bottom: 2rem; padding: 1.5rem; background: linear-gradient(135deg, var(--color-primary) 0%, #525252 100%); color: white; border-radius: 0.75rem;">
         <h3 style="margin: 0 0 0.75rem 0; font-size: 1.5rem;">${title}</h3>
         <p style="margin: 0; opacity: 0.9; line-height: 1.5;">
-          Basierend auf Ihrer Anfrage: "<em>${this.truncateText(query, 100)}</em>"
+          Basierend auf Ihrer Anfrage: "<em>${Utils.truncateText(query, 100)}</em>"
         </p>
       </div>
     `;
@@ -1188,7 +1340,7 @@ class AIQueryInterface {
           <h4 style="margin: 0 0 1rem 0; color: var(--color-primary);">
             ${mode === 'workflow' ? 'Szenario-Analyse' : 'Problem-Analyse'}
           </h4>
-          <div style="margin: 0; line-height: 1.6; white-space: pre-wrap; word-wrap: break-word;">${this.sanitizeText(analysisField)}</div>
+          <div style="margin: 0; line-height: 1.6; white-space: pre-wrap; word-wrap: break-word;">${Utils.sanitizeText(analysisField)}</div>
         </div>
       `;
     }
@@ -1199,7 +1351,7 @@ class AIQueryInterface {
           <h4 style="margin: 0 0 1rem 0; color: var(--color-accent);">
             ${mode === 'workflow' ? 'Untersuchungsansatz' : 'Lösungsansatz'}
           </h4>
-          <div style="margin: 0; line-height: 1.6; white-space: pre-wrap; word-wrap: break-word;">${this.sanitizeText(recommendation.investigation_approach)}</div>
+          <div style="margin: 0; line-height: 1.6; white-space: pre-wrap; word-wrap: break-word;">${Utils.sanitizeText(recommendation.investigation_approach)}</div>
         </div>
       `;
     }
@@ -1210,7 +1362,7 @@ class AIQueryInterface {
           <h4 style="margin: 0 0 1rem 0; color: var(--color-warning);">
             ${mode === 'workflow' ? 'Kritische Überlegungen' : 'Wichtige Voraussetzungen'}
           </h4>
-          <div style="margin: 0; line-height: 1.6; white-space: pre-wrap; word-wrap: break-word;">${this.sanitizeText(recommendation.critical_considerations)}</div>
+          <div style="margin: 0; line-height: 1.6; white-space: pre-wrap; word-wrap: break-word;">${Utils.sanitizeText(recommendation.critical_considerations)}</div>
         </div>
       `;
     }
@@ -1231,7 +1383,7 @@ class AIQueryInterface {
           <span class="badge" style="background-color: var(--color-concept); color: white; font-size: 0.625rem;">Hintergrundwissen</span>
         </div>
         <div style="margin: 0; font-size: 0.8125rem; line-height: 1.5; color: var(--color-text-secondary); white-space: pre-wrap; word-wrap: break-word;">
-          ${this.sanitizeText(concept.relevance)}
+          ${Utils.sanitizeText(concept.relevance)}
         </div>
       </div>
     `).join('');
@@ -1245,36 +1397,56 @@ class AIQueryInterface {
   }
 
   renderWorkflowPhases(toolsByPhase, phaseOrder, phaseNames) {
+    const phaseColors = [
+      'var(--color-primary)', 
+      'var(--color-accent)', 
+      'var(--color-warning)',
+      'var(--color-method)',
+      'var(--color-hosted)',
+      'var(--color-concept)'
+    ];
+
     return phaseOrder.map((phase, index) => {
       const phaseTools = toolsByPhase[phase];
       if (phaseTools.length === 0) return '';
 
-      console.log(`[AI DEBUG] Phase ${phase} tools:`, phaseTools.map(t => ({name: t.name, hasConfidence: !!t.confidence})));
+      const phaseColor = phaseColors[index % phaseColors.length];
 
       return `
-        <div class="workflow-phase">
-          <div class="phase-header">
-            <div class="phase-number">${index + 1}</div>
-            <div class="phase-info">
-              <h3 class="phase-title">${phaseNames[phase]}</h3>
-              <div class="phase-tools">
-                ${phaseTools.map(tool => {
-                  console.log(`[AI DEBUG] Rendering tool ${tool.name} with confidence:`, tool.confidence);
-                  return this.renderWorkflowTool(tool);
-                }).join('')}
+        <div class="workflow-phase" style="margin-bottom: 2rem;">
+          <div class="phase-header" style="display: flex; align-items: flex-start; gap: 1rem; padding: 1.5rem; background-color: var(--color-bg); border: 2px solid var(--color-border); border-radius: 0.75rem; transition: var(--transition-medium);" 
+               onmouseover="this.style.borderColor='${phaseColor}'; this.style.boxShadow='var(--shadow-md)';"
+               onmouseout="this.style.borderColor='var(--color-border)'; this.style.boxShadow='';">
+            
+            <div class="phase-number" style="width: 2rem; height: 2rem; background: ${phaseColor}; color: white; border-radius: 50%; display: flex; align-items: center; justify-content: center; font-weight: 600; flex-shrink: 0; box-shadow: var(--shadow-sm);">
+              ${index + 1}
+            </div>
+            
+            <div class="phase-info" style="display: flex; flex-direction: column; gap: 0.75rem; flex: 1; min-width: 0;">
+              <h3 class="phase-title" style="font-weight: 600; margin: 0; color: var(--color-text); font-size: 1.25rem;">
+                ${phaseNames[phase]}
+              </h3>
+              
+              <div class="phase-tools" style="display: grid; grid-template-columns: repeat(auto-fit, minmax(300px, 1fr)); gap: 1rem;">
+                ${phaseTools.map(tool => this.renderWorkflowTool(tool)).join('')}
               </div>
             </div>
           </div>
-          ${index < phaseOrder.length - 1 ? '<div class="workflow-arrow">↓</div>' : ''}
+          
+          ${index < phaseOrder.length - 1 ? `
+            <div class="workflow-arrow" style="display: flex; justify-content: center; margin: 1rem 0; color: ${phaseColor};">
+              <svg width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2">
+                <line x1="12" y1="5" x2="12" y2="19"/>
+                <polyline points="19 12 12 19 5 12"/>
+              </svg>
+            </div>
+          ` : ''}
         </div>
       `;
     }).join('');
   }
 
   renderWorkflowTool(tool) {
-    console.log(`[AI DEBUG] renderWorkflowTool called for ${tool.name}, confidence:`, tool.confidence);
-    
-    const hasValidProjectUrl = isToolHosted(tool);
     const priorityColors = {
       high: 'var(--color-error)',
       medium: 'var(--color-warning)', 
@@ -1284,32 +1456,39 @@ class AIQueryInterface {
     const priority = tool.recommendation ? tool.recommendation.priority : tool.priority;
     const confidenceTooltip = tool.confidence ? this.renderConfidenceTooltip(tool.confidence) : '';
     
-    console.log(`[AI DEBUG] Priority: ${priority}, Confidence tooltip:`, confidenceTooltip ? 'generated' : 'empty');
-
+    const cardClass = this.getToolClass(tool, 'recommendation');
+    
     return `
-      <div class="tool-recommendation ${this.getToolClass(tool, 'recommendation')}" onclick="window.showToolDetails('${tool.name}')" style="position: relative;">
-        <div class="tool-rec-header">
-          <h4 class="tool-rec-name">
-            ${tool.icon ? `<span style="margin-right: 0.5rem;">${tool.icon}</span>` : ''}
+      <div class="tool-recommendation ${cardClass}" onclick="window.showToolDetails('${tool.name}')" 
+           style="position: relative; cursor: pointer; transition: var(--transition-fast);"
+           onmouseover="this.style.transform='translateY(-2px)'; this.style.boxShadow='var(--shadow-md)';"
+           onmouseout="this.style.transform='translateY(0)'; this.style.boxShadow='';">
+        
+        <div class="tool-rec-header" style="display: flex; justify-content: space-between; align-items: flex-start; margin-bottom: 0.75rem;">
+          <h4 class="tool-rec-name" style="font-weight: 600; font-size: 1rem; margin: 0; color: var(--color-text); flex: 1; margin-right: 0.75rem;">
+            ${tool.icon ? `<span style="margin-right: 0.5rem; font-size: 1.125rem;">${tool.icon}</span>` : ''}
             ${tool.name}
           </h4>
-          <div class="flex items-center gap-2">
+          <div style="display: flex; align-items: center; gap: 0.375rem; flex-shrink: 0;">
             <span class="tool-rec-priority ${priority}" 
-                  style="background-color: ${priorityColors[priority]}; color: white; padding: 0.25rem 0.5rem; border-radius: 1rem; font-size: 0.75rem; position: relative; display: flex; align-items: center; gap: 0.25rem;">
+                  style="background-color: ${priorityColors[priority]}; color: white; padding: 0.25rem 0.5rem; border-radius: 1rem; font-size: 0.75rem; font-weight: 500; text-transform: uppercase; position: relative; display: flex; align-items: center; gap: 0.25rem;">
               ${priority}
               ${confidenceTooltip}
             </span>
           </div>
         </div>
         
-        <div class="tool-rec-justification" style="background-color: var(--color-bg-tertiary); padding: 0.75rem; border-radius: 0.375rem; border-left: 3px solid var(--color-primary); margin: 0.75rem 0; font-style: italic; white-space: pre-wrap; word-wrap: break-word;">
-          "${this.sanitizeText(tool.justification || (tool.recommendation && tool.recommendation.justification) || `Empfohlen für ${tool.phase}`)}"
+        <div class="tool-rec-justification" style="background-color: var(--color-bg-tertiary); padding: 0.75rem; border-radius: 0.375rem; border-left: 3px solid var(--color-primary); margin: 0.75rem 0; font-style: italic; white-space: pre-wrap; word-wrap: break-word; font-size: 0.875rem; line-height: 1.5; color: var(--color-text-secondary);">
+          "${Utils.sanitizeText(tool.justification || (tool.recommendation && tool.recommendation.justification) || `Empfohlen für ${tool.phase}`)}"
         </div>
         
-        <div style="font-size: 0.75rem; color: var(--color-text-secondary);">
-          ${this.renderToolBadges(tool)}
-          <div style="margin-top: 0.5rem;">
-            ${tool.type === 'method' ? 'Methode' : tool.platforms.join(', ') + ' • ' + tool.license}
+        <div style="font-size: 0.75rem; color: var(--color-text-secondary); margin-top: auto;">
+          <div style="display: flex; flex-wrap: wrap; gap: 0.375rem; margin-bottom: 0.5rem;">
+            ${this.renderToolBadges(tool)}
+          </div>
+          <div style="display: flex; justify-content: space-between; align-items: center;">
+            <span>${tool.type === 'method' ? 'Methode' : tool.platforms.slice(0, 2).join(', ')}</span>
+            <span>${tool.skillLevel}</span>
           </div>
         </div>
       </div>
@@ -1325,18 +1504,13 @@ class AIQueryInterface {
           const fullTool = tools.find(t => t.name === toolRec.name);
           if (!fullTool) return '';
           
-          return this.renderDetailedTool(fullTool, {
-            ...toolRec,
-            confidence: toolRec.confidence 
-          }, index + 1);
+          return this.renderDetailedTool(fullTool, toolRec, index + 1);
         }).join('')}
       </div>
     `;
   }
 
   renderDetailedTool(tool, recommendation, rank) {
-    console.log(`[AI DEBUG] renderDetailedTool called for ${tool.name}, recommendation confidence:`, recommendation.confidence);
-    
     const rankColors = { 1: 'var(--color-accent)', 2: 'var(--color-primary)', 3: 'var(--color-warning)' };
     const suitabilityColors = { high: 'var(--color-accent)', medium: 'var(--color-warning)', low: 'var(--color-text-secondary)' };
     
@@ -1361,11 +1535,11 @@ class AIQueryInterface {
 
         <div style="margin-bottom: 1.5rem;">
           <h4 style="margin: 0.8rem 0 0.75rem 0; color: var(--color-accent);">Warum diese Methode?</h4>
-          <div style="margin: 0; line-height: 1.6; white-space: pre-wrap; word-wrap: break-word;">${this.sanitizeText(recommendation.detailed_explanation)}</div>
+          <div style="margin: 0; line-height: 1.6; white-space: pre-wrap; word-wrap: break-word;">${Utils.sanitizeText(recommendation.detailed_explanation)}</div>
           
           ${recommendation.implementation_approach ? `
             <h4 style="margin: 0.8rem 0 0.75rem 0; color: var(--color-primary);">Anwendungsansatz</h4>
-            <div style="margin: 0; line-height: 1.6; white-space: pre-wrap; word-wrap: break-word;">${this.sanitizeText(recommendation.implementation_approach)}</div>
+            <div style="margin: 0; line-height: 1.6; white-space: pre-wrap; word-wrap: break-word;">${Utils.sanitizeText(recommendation.implementation_approach)}</div>
           ` : ''}
         </div>
 
@@ -1376,6 +1550,102 @@ class AIQueryInterface {
     `;
   }
 
+  renderConfidenceTooltip(confidence) {
+    if (!confidence || typeof confidence.overall !== 'number') return '';
+    
+    const confidenceColor = Utils.getConfidenceColor(confidence.overall);
+    const tooltipId = `tooltip-${Math.random().toString(36).substr(2, 9)}`;
+    
+    return `
+      <span class="confidence-tooltip-trigger" 
+            style="display: inline-flex; align-items: center; gap: 0.125rem; cursor: help; margin-left: 0.25rem; position: relative;"
+            onmouseenter="this.querySelector('.confidence-tooltip').style.display = 'block'"
+            onmouseleave="this.querySelector('.confidence-tooltip').style.display = 'none'"
+            onclick="event.stopPropagation();">
+        <div style="width: 6px; height: 6px; border-radius: 50%; background-color: ${confidenceColor}; flex-shrink: 0;"></div>
+        <span style="font-size: 0.625rem; color: white; font-weight: 600; text-shadow: 0 1px 2px rgba(0,0,0,0.5);">${confidence.overall}%</span>
+        
+        <div class="confidence-tooltip" 
+             style="display: none; position: absolute; top: 100%; right: 0; z-index: 1001; background: var(--color-bg); border: 1px solid var(--color-border); border-radius: 0.5rem; padding: 1rem; min-width: 320px; max-width: 400px; box-shadow: var(--shadow-lg); font-size: 0.75rem; color: var(--color-text); margin-top: 0.5rem;">
+          <div style="display: flex; justify-content: space-between; align-items: center; margin-bottom: 0.75rem;">
+            <strong style="font-size: 0.875rem; color: var(--color-primary);">KI-Vertrauenswertung</strong>
+            <span style="background-color: ${confidenceColor}; color: white; font-weight: 600; padding: 0.125rem 0.375rem; border-radius: 0.25rem; font-size: 0.625rem;">${confidence.overall}%</span>
+          </div>
+          
+          <div style="display: grid; grid-template-columns: 1fr; gap: 0.625rem; margin-bottom: 0.75rem;">
+            <div style="background: var(--color-bg-secondary); padding: 0.5rem; border-radius: 0.375rem; border-left: 3px solid var(--color-accent);">
+              <div style="display: flex; justify-content: space-between; align-items: center; margin-bottom: 0.25rem;">
+                <span style="font-weight: 600; font-size: 0.6875rem; color: var(--color-text);">🔍 Semantische Relevanz</span>
+                <strong style="color: var(--color-accent);">${confidence.semanticRelevance}%</strong>
+              </div>
+              <div style="font-size: 0.625rem; color: var(--color-text-secondary); line-height: 1.3;">
+                Wie gut die Tool-Beschreibung semantisch zu Ihrer Anfrage passt
+              </div>
+            </div>
+            
+            <div style="background: var(--color-bg-secondary); padding: 0.5rem; border-radius: 0.375rem; border-left: 3px solid var(--color-primary);">
+              <div style="display: flex; justify-content: space-between; align-items: center; margin-bottom: 0.25rem;">
+                <span style="font-weight: 600; font-size: 0.6875rem; color: var(--color-text);">🎯 Aufgaben-Eignung</span>
+                <strong style="color: var(--color-primary);">${confidence.taskSuitability}%</strong>
+              </div>
+              <div style="font-size: 0.625rem; color: var(--color-text-secondary); line-height: 1.3;">
+                KI-bewertete Eignung für Ihre spezifische Aufgabenstellung
+              </div>
+            </div>
+          </div>
+          
+          ${confidence.strengthIndicators && confidence.strengthIndicators.length > 0 ? `
+            <div style="margin-bottom: 0.75rem; padding: 0.5rem; background: var(--color-oss-bg); border-radius: 0.375rem; border-left: 3px solid var(--color-accent);">
+              <strong style="color: var(--color-accent); font-size: 0.6875rem; display: flex; align-items: center; gap: 0.25rem; margin-bottom: 0.375rem;">
+                <span>✓</span> Stärken dieser Empfehlung:
+              </strong>
+              <ul style="margin: 0; padding-left: 1rem; font-size: 0.625rem; line-height: 1.4; color: var(--color-text);">
+                ${confidence.strengthIndicators.slice(0, 3).map(s => `<li style="margin-bottom: 0.25rem;">${Utils.sanitizeText(s)}</li>`).join('')}
+              </ul>
+            </div>
+          ` : ''}
+          
+          ${confidence.uncertaintyFactors && confidence.uncertaintyFactors.length > 0 ? `
+            <div style="padding: 0.5rem; background: var(--color-hosted-bg); border-radius: 0.375rem; border-left: 3px solid var(--color-warning);">
+              <strong style="color: var(--color-warning); font-size: 0.6875rem; display: flex; align-items: center; gap: 0.25rem; margin-bottom: 0.375rem;">
+                <span>⚠</span> Mögliche Einschränkungen:
+              </strong>
+              <ul style="margin: 0; padding-left: 1rem; font-size: 0.625rem; line-height: 1.4; color: var(--color-text);">
+                ${confidence.uncertaintyFactors.slice(0, 3).map(f => `<li style="margin-bottom: 0.25rem;">${Utils.sanitizeText(f)}</li>`).join('')}
+              </ul>
+            </div>
+          ` : ''}
+          
+          <div style="margin-top: 0.75rem; padding-top: 0.75rem; border-top: 1px solid var(--color-border); font-size: 0.625rem; color: var(--color-text-secondary); text-align: center;">
+            Forensisch fundierte KI-Analyse
+          </div>
+        </div>
+      </span>
+    `;
+  }
+
+  renderWorkflowSuggestion(suggestion) {
+    if (!suggestion) return '';
+    
+    return `
+      <div class="card" style="margin-top: 2rem; border-left: 4px solid var(--color-accent);">
+        <h4 style="margin: 0 0 1rem 0; color: var(--color-accent);">Workflow-Empfehlung</h4>
+        <div style="margin: 0; line-height: 1.6; white-space: pre-wrap; word-wrap: break-word;">${Utils.sanitizeText(suggestion)}</div>
+      </div>
+    `;
+  }
+
+  renderAdditionalConsiderations(considerations) {
+    if (!considerations) return '';
+    
+    return `
+      <div class="card" style="margin-top: 2rem; background-color: var(--color-bg-secondary); border-left: 4px solid var(--color-text-secondary);">
+        <h4 style="margin: 0 0 1rem 0; color: var(--color-text-secondary);">Zusätzliche Überlegungen</h4>
+        <div style="margin: 0; line-height: 1.6; white-space: pre-wrap; word-wrap: break-word;">${Utils.sanitizeText(considerations)}</div>
+      </div>
+    `;
+  }
+
   renderProsAndCons(pros, cons) {
     if (!pros?.length && !cons?.length) return '';
     
@@ -1385,7 +1655,7 @@ class AIQueryInterface {
           <div style="background-color: var(--color-oss-bg); padding: 1rem; border-radius: 0.5rem; border-left: 3px solid var(--color-accent);">
             <h5 style="margin: 0 0 0.5rem 0; color: var(--color-accent); font-size: 0.875rem;">✓ Vorteile</h5>
             <ul style="margin: 0; padding-left: 1rem;">
-              ${pros.map(pro => `<li style="margin-bottom: 0.25rem;">${this.sanitizeText(pro)}</li>`).join('')}
+              ${pros.map(pro => `<li style="margin-bottom: 0.25rem;">${Utils.sanitizeText(pro)}</li>`).join('')}
             </ul>
           </div>
         ` : ''}
@@ -1393,7 +1663,7 @@ class AIQueryInterface {
           <div style="background-color: var(--color-hosted-bg); padding: 1rem; border-radius: 0.5rem; border-left: 3px solid var(--color-warning);">
             <h5 style="margin: 0 0 0.5rem 0; color: var(--color-warning); font-size: 0.875rem;">✗ Nachteile</h5>
             <ul style="margin: 0; padding-left: 1rem;">
-              ${cons.map(con => `<li style="margin-bottom: 0.25rem;">${this.sanitizeText(con)}</li>`).join('')}
+              ${cons.map(con => `<li style="margin-bottom: 0.25rem;">${Utils.sanitizeText(con)}</li>`).join('')}
             </ul>
           </div>
         ` : ''}
@@ -1405,7 +1675,7 @@ class AIQueryInterface {
     const isMethod = tool.type === 'method';
     
     return `
-      <div style="display: grid; grid-template-columns: repeat(auto-fit, minmax(150px, 1fr)); gap: 0.75rem; font-size: 0.8125rem; color: var(--color-text-secondary); margin-bottom: 1rem;">
+      <div style="display: grid; grid-template-columns: repeat(auto-fit, minmax(150px, 1fr)); gap: 0.75rem; font-size: 0.8125rem; color: var(--color-text-secondary); margin-bottom: 1rem; padding: 0.75rem; background-color: var(--color-bg-secondary); border-radius: 0.375rem;">
         ${!isMethod ? `<div><strong>Plattformen:</strong> ${tool.platforms.join(', ')}</div>` : ''}
         <div><strong>Skill Level:</strong> ${tool.skillLevel}</div>
         ${!isMethod ? `<div><strong>Lizenz:</strong> ${tool.license}</div>` : ''}
@@ -1418,29 +1688,7 @@ class AIQueryInterface {
     return `
       <div style="background-color: var(--color-bg-secondary); padding: 1rem; border-radius: 0.5rem; margin-bottom: 1rem;">
         <h5 style="margin: 0 0 0.5rem 0; color: var(--color-text-secondary); font-size: 0.875rem;">Alternative Ansätze</h5>
-        <div style="margin: 0; line-height: 1.6; white-space: pre-wrap; word-wrap: break-word;">${this.sanitizeText(alternatives)}</div>
-      </div>
-    `;
-  }
-
-  renderWorkflowSuggestion(suggestion) {
-    if (!suggestion) return '';
-    
-    return `
-      <div class="card" style="margin-top: 2rem; border-left: 4px solid var(--color-accent);">
-        <h4 style="margin: 0 0 1rem 0; color: var(--color-accent);">Workflow-Empfehlung</h4>
-        <div style="margin: 0; line-height: 1.6; white-space: pre-wrap; word-wrap: break-word;">${this.sanitizeText(suggestion)}</div>
-      </div>
-    `;
-  }
-
-  renderAdditionalConsiderations(considerations) {
-    if (!considerations) return '';
-    
-    return `
-      <div class="card" style="margin-top: 2rem; background-color: var(--color-bg-secondary); border-left: 4px solid var(--color-text-secondary);">
-        <h4 style="margin: 0 0 1rem 0; color: var(--color-text-secondary);">Zusätzliche Überlegungen</h4>
-        <div style="margin: 0; line-height: 1.6; white-space: pre-wrap; word-wrap: break-word;">${this.sanitizeText(considerations)}</div>
+        <div style="margin: 0; line-height: 1.6; white-space: pre-wrap; word-wrap: break-word;">${Utils.sanitizeText(alternatives)}</div>
       </div>
     `;
   }
@@ -1465,32 +1713,6 @@ class AIQueryInterface {
     return badges;
   }
 
-  sanitizeText(text) {
-    if (typeof text !== 'string') return '';
-    
-    return text
-      // Remove markdown headers
-      .replace(/^#{1,6}\s+/gm, '')
-      // Remove markdown lists (bullet points and numbered)
-      .replace(/^\s*[-*+]\s+/gm, '')
-      .replace(/^\s*\d+\.\s+/gm, '')
-      // Remove markdown bold/italic
-      .replace(/\*\*(.+?)\*\*/g, '$1')
-      .replace(/\*(.+?)\*/g, '$1')
-      .replace(/__(.+?)__/g, '$1')
-      .replace(/_(.+?)_/g, '$1')
-      // Remove markdown links
-      .replace(/\[([^\]]+)\]\([^)]+\)/g, '$1')
-      // Remove code blocks
-      .replace(/```[\s\S]*?```/g, '[CODE BLOCK]')
-      .replace(/`([^`]+)`/g, '$1')
-      // Remove HTML tags
-      .replace(/<[^>]+>/g, '')
-      // Clean up excessive whitespace but preserve paragraphs
-      .replace(/\n\s*\n\s*\n/g, '\n\n')
-      .trim();
-  }
-
   getToolClass(tool, context = 'card') {
     const isMethod = tool.type === 'method';
     const hasValidProjectUrl = isToolHosted(tool);
@@ -1508,89 +1730,67 @@ class AIQueryInterface {
     }
   }
 
-  getSuitabilityText(score, confidence = null) {
+  getSuitabilityText(score) {
     const texts = {
       high: 'GUT GEEIGNET',
       medium: 'GEEIGNET', 
       low: 'VIELLEICHT GEEIGNET'
     };
-    const baseText = texts[score] || 'GEEIGNET';
-    
-    if (confidence) {
-      return `${baseText} ${this.renderConfidenceTooltip(confidence, 'suitability')}`;
-    }
-    return baseText;
-  }
-
-  escapeHtml(text) {
-    if (typeof text !== 'string') return '';
-    const div = document.createElement('div');
-    div.textContent = text;
-    return div.innerHTML;
-  }
-
-  truncateText(text, maxLength) {
-    if (text.length <= maxLength) return text;
-    return text.slice(0, maxLength) + '...';
-  }
-
-  formatDuration(ms) {
-    if (ms < 1000) return '< 1s';
-    if (ms < 60000) return `${Math.ceil(ms / 1000)}s`;
-    return `${Math.ceil(ms / 60000)}m`;
-  }
-
-  showElement(element) {
-    if (element) {
-      element.style.display = 'block';
-      element.classList.remove('hidden');
-    }
-  }
-
-  hideElement(element) {
-    if (element) {
-      element.style.display = 'none';
-      element.classList.add('hidden');
-    }
+    return texts[score] || 'GEEIGNET';
   }
 
   showLoading() {
-    this.showElement(this.elements.loading);
+    Utils.showElement(this.elements.loading);
   }
 
   hideLoading() {
-    this.hideElement(this.elements.loading);
+    Utils.hideElement(this.elements.loading);
   }
 
   showResults() {
-    this.showElement(this.elements.results);
+    Utils.showElement(this.elements.results);
   }
 
   hideResults() {
-    this.hideElement(this.elements.results);
-    this.hideElement(this.elements.error);
+    Utils.hideElement(this.elements.results);
+    Utils.hideElement(this.elements.error);
   }
 
   showError(message) {
     if (this.elements.errorMessage) this.elements.errorMessage.textContent = message;
-    this.showElement(this.elements.error);
+    Utils.showElement(this.elements.error);
   }
 
   hideError() {
-    this.hideElement(this.elements.error);
+    Utils.hideElement(this.elements.error);
+  }
+
+  restoreAIResults() {
+    if (this.currentRecommendation && this.elements.results) {
+      this.showResults();
+      
+      if (this.currentRecommendation.auditTrail) {
+        setTimeout(() => {
+          try {
+            this.renderAuditTrail(this.currentRecommendation.auditTrail);
+            console.log('[AI Interface] Audit trail restored successfully');
+          } catch (error) {
+            console.error('[AI Interface] Audit trail restore failed:', error);
+          }
+        }, 100);
+      }
+      
+      this.hideLoading();
+      this.hideError();
+    }
   }
 }
 
 document.addEventListener('DOMContentLoaded', () => {
   const aiInterface = new AIQueryInterface();
   
-  window.restoreAIResults = () => {
-    if (aiInterface.currentRecommendation && aiInterface.elements.results) {
-      aiInterface.showResults();
-      aiInterface.hideLoading();
-      aiInterface.hideError();
-    }
-  };
-  window.isToolHosted = window.isToolHosted || isToolHosted
-});
-</script>
\ No newline at end of file
+  window.restoreAIResults = () => aiInterface.restoreAIResults();
+  window.isToolHosted = window.isToolHosted || isToolHosted;
+  
+  console.log('[AI Interface] Initialized successfully');
+});
\ No newline at end of file
diff --git a/src/components/ToolFilters.astro b/src/components/ToolFilters.astro
index be956ea..1ee5008 100644
--- a/src/components/ToolFilters.astro
+++ b/src/components/ToolFilters.astro
@@ -25,33 +25,55 @@ const sortedTags = Object.entries(tagFrequency)
 
 <div class="filters-container">
   <!-- Search Section -->
-  <div class="filter-section">
-    <div class="filter-card-compact">
-      <div class="filter-header-compact">
-        <h3>🔍 Suche</h3>
-      </div>
-      <div class="search-wrapper">
-        <div class="search-icon">
-          <svg width="18" height="18" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2">
-            <circle cx="11" cy="11" r="8"/>
-            <line x1="21" y1="21" x2="16.65" y2="16.65"/>
-          </svg>
+    <div class="filter-section">
+      <div class="filter-card-compact">
+        <div class="filter-header-compact">
+          <h3>🔍 Suche</h3>
+        </div>
+        <div class="search-row">
+          <div class="search-wrapper">
+            <div class="search-icon">
+              <svg width="18" height="18" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2">
+                <circle cx="11" cy="11" r="8"/>
+                <line x1="21" y1="21" x2="16.65" y2="16.65"/>
+              </svg>
+            </div>
+            <input 
+              type="text" 
+              id="search-input" 
+              placeholder="Software, Beschreibung oder Tags durchsuchen..."
+              class="search-input"
+            />
+            <button id="clear-search" class="search-clear hidden" title="Suche löschen">
+              <svg width="16" height="16" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2">
+                <line x1="18" y1="6" x2="6" y2="18"/>
+                <line x1="6" y1="6" x2="18" y2="18"/>
+              </svg>
+            </button>
+          </div>
+          
+          <!-- Semantic Search Toggle - Inline -->
+          <div id="semantic-search-container" class="semantic-search-inline hidden">
+            <label class="semantic-toggle-wrapper" title="Semantische Suche verwendet Embeddings. Dadurch kann mit natürlicher Sprache/Begriffen gesucht werden, die Ergebnisse richten sich nach der euklidischen Distanz.">
+              <input type="checkbox" id="semantic-search-enabled" disabled/>
+              <div class="semantic-checkbox-custom"></div>
+              <span class="semantic-toggle-label">
+                <svg width="16" height="16" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2">
+                  <path d="M9 11H5a2 2 0 0 0-2 2v7a2 2 0 0 0 2 2h14a2 2 0 0 0 2-2v-7a2 2 0 0 0-2-2h-4"/>
+                  <path d="M9 11V7a3 3 0 0 1 6 0v4"/>
+                </svg>
+                Semantisch
+              </span>
+            </label>
+          </div>
+        </div>
+        
+        <!-- Status Display -->
+        <div id="semantic-status" class="semantic-status hidden">
+          <span class="semantic-results-count"></span>
         </div>
-        <input 
-          type="text" 
-          id="search-input" 
-          placeholder="Software, Beschreibung oder Tags durchsuchen..."
-          class="search-input"
-        />
-        <button id="clear-search" class="search-clear hidden" title="Suche löschen">
-          <svg width="16" height="16" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2">
-            <line x1="18" y1="6" x2="6" y2="18"/>
-            <line x1="6" y1="6" x2="18" y2="18"/>
-          </svg>
-        </button>
       </div>
     </div>
-  </div>
 
   <!-- Primary Filters Section - ONLY Domain and Phase -->
   <div class="filter-section">
@@ -289,6 +311,10 @@ const sortedTags = Object.entries(tagFrequency)
     const elements = {
       searchInput: document.getElementById('search-input'),
       clearSearch: document.getElementById('clear-search'),
+      semanticContainer: document.getElementById('semantic-search-container'),
+      semanticCheckbox: document.getElementById('semantic-search-enabled'),
+      semanticStatus: document.getElementById('semantic-status'),
+      semanticResultsCount: document.querySelector('.semantic-results-count'),
       domainSelect: document.getElementById('domain-select'),
       phaseSelect: document.getElementById('phase-select'),
       typeSelect: document.getElementById('type-select'),
@@ -324,6 +350,48 @@ const sortedTags = Object.entries(tagFrequency)
     let selectedTags = new Set();
     let selectedPhase = '';
     let isTagCloudExpanded = false;
+    let semanticSearchEnabled = false;
+    let semanticSearchAvailable = false;
+    let lastSemanticResults = null;
+    
+    async function checkEmbeddingsAvailability() {
+      try {
+        const res = await fetch('/api/ai/embeddings-status');
+        const { embeddings } = await res.json();
+        semanticSearchAvailable = embeddings?.enabled && embeddings?.initialized;
+
+        if (semanticSearchAvailable) {
+          elements.semanticContainer.classList.remove('hidden');
+          elements.semanticCheckbox.disabled = false;
+        }
+      } catch (err) {
+        console.error('[EMBEDDINGS] Status check failed:', err);
+      }
+    }
+    
+    async function performSemanticSearch(query) {
+      if (!semanticSearchAvailable || !query.trim()) {
+        return null;
+      }
+      
+      try {
+        const response = await fetch('/api/search/semantic', {
+          method: 'POST',
+          headers: { 'Content-Type': 'application/json' },
+          body: JSON.stringify({ query: query.trim() })
+        });
+        
+        if (!response.ok) {
+          throw new Error(`HTTP ${response.status}`);
+        }
+        
+        const data = await response.json();
+        return data.results || [];
+      } catch (error) {
+        console.error('[SEMANTIC] Search failed:', error);
+        return null;
+      }
+    }
     
     function toggleCollapsible(toggleBtn, content, storageKey) {
       const isCollapsed = toggleBtn.getAttribute('data-collapsed') === 'true';
@@ -494,7 +562,18 @@ const sortedTags = Object.entries(tagFrequency)
         : `${count} von ${total} Tools`;
     }
     
-    function filterTools() {
+    function updateSemanticStatus(results) {
+      if (!elements.semanticStatus || !elements.semanticResultsCount) return;
+      
+      if (semanticSearchEnabled && results?.length > 0) {
+        elements.semanticStatus.classList.remove('hidden');
+        elements.semanticResultsCount.textContent = `${results.length} semantische Treffer`;
+      } else {
+        elements.semanticStatus.classList.add('hidden');
+      }
+    }
+    
+    async function filterTools() {
       const searchTerm = elements.searchInput.value.trim().toLowerCase();
       const selectedDomain = elements.domainSelect.value;
       const selectedPhaseFromSelect = elements.phaseSelect.value;
@@ -508,15 +587,29 @@ const sortedTags = Object.entries(tagFrequency)
       
       const activePhase = selectedPhaseFromSelect || selectedPhase;
       
-      const filtered = window.toolsData.filter(tool => {
-        if (searchTerm && !(
-          tool.name.toLowerCase().includes(searchTerm) ||
-          tool.description.toLowerCase().includes(searchTerm) ||
-          (tool.tags || []).some(tag => tag.toLowerCase().includes(searchTerm))
-        )) {
-          return false;
-        }
+      let filteredTools = window.toolsData;
+      let semanticResults = null;
+      
+      if (semanticSearchEnabled && semanticSearchAvailable && searchTerm) {
+        semanticResults = await performSemanticSearch(searchTerm);
+        lastSemanticResults = semanticResults;
         
+        if (semanticResults?.length > 0) {
+          filteredTools = [...semanticResults];
+        }
+      } else {
+        lastSemanticResults = null;
+        
+        if (searchTerm) {
+          filteredTools = window.toolsData.filter(tool =>
+            tool.name.toLowerCase().includes(searchTerm) ||
+            tool.description.toLowerCase().includes(searchTerm) ||
+            (tool.tags || []).some(tag => tag.toLowerCase().includes(searchTerm))
+          );
+        }
+      }
+      
+      filteredTools = filteredTools.filter(tool => {
         if (selectedDomain && !(tool.domains || []).includes(selectedDomain)) {
           return false;
         }
@@ -560,13 +653,29 @@ const sortedTags = Object.entries(tagFrequency)
         return true;
       });
       
-      const finalResults = searchTerm && window.prioritizeSearchResults 
-        ? window.prioritizeSearchResults(filtered, searchTerm)
-        : filtered;
+      if (semanticSearchEnabled && lastSemanticResults) {
+        filteredTools.sort(
+          (a, b) => (b._semanticSimilarity || 0) - (a._semanticSimilarity || 0)
+        );
+      }
+
+      const finalResults = semanticSearchEnabled && lastSemanticResults
+        ? filteredTools
+        : (searchTerm && window.prioritizeSearchResults
+            ? window.prioritizeSearchResults(filteredTools, searchTerm)
+            : filteredTools);
       
       updateResultsCounter(finalResults.length);
+      updateSemanticStatus(lastSemanticResults);
       
-      window.dispatchEvent(new CustomEvent('toolsFiltered', { detail: finalResults }));
+      window.dispatchEvent(
+        new CustomEvent('toolsFiltered', {
+          detail: {
+            tools: finalResults,
+            semanticSearch: semanticSearchEnabled && !!lastSemanticResults,
+          },
+        })
+      );
     }
     
     function resetPrimaryFilters() {
@@ -599,6 +708,10 @@ const sortedTags = Object.entries(tagFrequency)
     function resetAllFilters() {
       elements.searchInput.value = '';
       elements.clearSearch.classList.add('hidden');
+      elements.semanticCheckbox.checked = false;
+      semanticSearchEnabled = false;
+      lastSemanticResults = null;
+      updateSemanticStatus(null);
       resetPrimaryFilters();
       resetAdvancedFilters();
       resetTags();
@@ -619,16 +732,29 @@ const sortedTags = Object.entries(tagFrequency)
       filterTools();
     });
     
+    if (elements.semanticCheckbox) {
+      elements.semanticCheckbox.addEventListener('change', (e) => {
+        semanticSearchEnabled = e.target.checked;
+        filterTools();
+      });
+    }
+    
     [elements.domainSelect, elements.phaseSelect, elements.typeSelect, elements.skillSelect, 
      elements.platformSelect, elements.licenseSelect, elements.accessSelect].forEach(select => {
-      select.addEventListener('change', filterTools);
+      if (select) {
+        select.addEventListener('change', filterTools);
+      }
     });
     
     [elements.hostedOnly, elements.knowledgebaseOnly].forEach(checkbox => {
-      checkbox.addEventListener('change', filterTools);
+      if (checkbox) {
+        checkbox.addEventListener('change', filterTools);
+      }
     });
     
-    elements.tagCloudToggle.addEventListener('click', toggleTagCloud);
+    if (elements.tagCloudToggle) {
+      elements.tagCloudToggle.addEventListener('click', toggleTagCloud);
+    }
     
     elements.tagCloudItems.forEach(item => {
       item.addEventListener('click', () => {
@@ -649,14 +775,24 @@ const sortedTags = Object.entries(tagFrequency)
           b.classList.toggle('active', b.getAttribute('data-view') === view);
         });
         
-        window.dispatchEvent(new CustomEvent('viewChanged', { detail: view }));
-        
-        if (view === 'hosted') {
-          const hosted = window.toolsData.filter(tool => isToolHosted(tool));        
-          window.dispatchEvent(new CustomEvent('toolsFiltered', { detail: hosted }));
+        if (window.switchToView) {
+          window.switchToView(view);
         } else {
-          filterTools();
+          console.error('switchToView function not available');
         }
+        
+        window.dispatchEvent(new CustomEvent('viewChanged', { 
+          detail: view,
+          triggeredByButton: true 
+        }));
+        
+        setTimeout(() => {
+          if (view === 'matrix') {
+            filterTools();
+          } else if (view === 'grid') {
+            filterTools();
+          }
+        }, 100); 
       });
     });
     
@@ -676,6 +812,7 @@ const sortedTags = Object.entries(tagFrequency)
     window.clearTagFilters = resetTags;
     window.clearAllFilters = resetAllFilters;
     
+    checkEmbeddingsAvailability();
     initializeCollapsible();
     initTagCloud();
     filterTagCloud();
diff --git a/src/components/ToolMatrix.astro b/src/components/ToolMatrix.astro
index 5e83e44..10db0eb 100644
--- a/src/components/ToolMatrix.astro
+++ b/src/components/ToolMatrix.astro
@@ -193,6 +193,16 @@ domains.forEach((domain: any) => {
 </div>
 
 <script define:vars={{ toolsData: tools, domainAgnosticSoftware, domainAgnosticTools }}>
+  // Ensure isToolHosted is available
+  if (!window.isToolHosted) {
+    window.isToolHosted = function(tool) {
+      return tool.projectUrl !== undefined && 
+             tool.projectUrl !== null && 
+             tool.projectUrl !== "" && 
+             tool.projectUrl.trim() !== "";
+    };
+  }
+
   function getSelectedPhase() {
     const activePhaseChip = document.querySelector('.phase-chip.active');
     return activePhaseChip ? activePhaseChip.getAttribute('data-phase') : '';
@@ -216,9 +226,7 @@ domains.forEach((domain: any) => {
     
     if (selectedDomain) {
       const domainRow = matrixTable.querySelector(`tr[data-domain="${selectedDomain}"]`);
-      if (domainRow) {
-        domainRow.classList.add('highlight-row');
-      }
+      if (domainRow) domainRow.classList.add('highlight-row');
     }
     
     if (selectedPhase) {
@@ -231,9 +239,7 @@ domains.forEach((domain: any) => {
         const columnIndex = phaseIndex + 1; 
         matrixTable.querySelectorAll(`tr`).forEach(row => {
           const cell = row.children[columnIndex];
-          if (cell) {
-            cell.classList.add('highlight-column');
-          }
+          if (cell) cell.classList.add('highlight-column');
         });
       }
     }
@@ -267,9 +273,7 @@ domains.forEach((domain: any) => {
     const params = new URLSearchParams();
     params.set('tool', toolSlug);
     params.set('view', view);
-    if (modal) {
-      params.set('modal', modal);
-    }
+    if (modal) params.set('modal', modal);
     return `${baseUrl}?${params.toString()}`;
   }
 
@@ -301,7 +305,7 @@ domains.forEach((domain: any) => {
     }
   }
 
-  window.showShareDialog = function(shareButton) {
+  function showShareDialog(shareButton) {
     const toolName = shareButton.getAttribute('data-tool-name');
     const context = shareButton.getAttribute('data-context');
     
@@ -438,16 +442,11 @@ domains.forEach((domain: any) => {
         copyToClipboard(url, btn);
       });
     });
-  };
+  }
 
-  window.toggleDomainAgnosticSection = toggleDomainAgnosticSection;
-
-  window.showToolDetails = function(toolName, modalType = 'primary') {
+  function showToolDetails(toolName, modalType = 'primary') {
     const tool = toolsData.find(t => t.name === toolName);
-    if (!tool) {
-      console.error('Tool not found:', toolName);
-      return;
-    }
+    if (!tool) return;
     
     const isMethod = tool.type === 'method';
     const isConcept = tool.type === 'concept';
@@ -462,10 +461,7 @@ domains.forEach((domain: any) => {
     };
     
     for (const [key, element] of Object.entries(elements)) {
-      if (!element) {
-        console.error(`Element not found: tool-${key}-${modalType}`);
-        return;
-      }
+      if (!element) return;
     }
     
     const iconHtml = tool.icon ? `<span class="mr-3 text-xl">${tool.icon}</span>` : '';
@@ -709,9 +705,9 @@ domains.forEach((domain: any) => {
     if (primaryActive && secondaryActive) {
       document.body.classList.add('modals-side-by-side');
     }
-  };
+  }
 
-  window.hideToolDetails = function(modalType = 'both') {
+  function hideToolDetails(modalType = 'both') {
     const overlay = document.getElementById('modal-overlay');
     const primaryModal = document.getElementById('tool-details-primary');
     const secondaryModal = document.getElementById('tool-details-secondary');
@@ -763,28 +759,44 @@ domains.forEach((domain: any) => {
     } else {
       document.body.classList.remove('modals-side-by-side');
     }
-  };
+  }
 
-  window.hideAllToolDetails = function() {
-    window.hideToolDetails('both');
-  };
+  function hideAllToolDetails() {
+    hideToolDetails('both');
+  }
+
+  // Register all functions globally
+  window.showToolDetails = showToolDetails;
+  window.hideToolDetails = hideToolDetails;
+  window.hideAllToolDetails = hideAllToolDetails;
+  window.toggleDomainAgnosticSection = toggleDomainAgnosticSection;
+  window.showShareDialog = showShareDialog;
+
+  // Register matrix-prefixed versions for delegation
+  window.matrixShowToolDetails = showToolDetails;
+  window.matrixHideToolDetails = hideToolDetails;
 
   window.addEventListener('viewChanged', (event) => {
     const view = event.detail;
     if (view === 'matrix') {
-      setTimeout(updateMatrixHighlighting, 100);
+      setTimeout(() => {
+        if (window.filterTools && typeof window.filterTools === 'function') {
+          window.filterTools();
+        } else {
+          const allTools = window.toolsData || [];
+          window.dispatchEvent(new CustomEvent('toolsFiltered', {
+            detail: {
+              tools: allTools,
+              semanticSearch: false
+            }
+          }));
+        }
+      }, 100);
     }
   });
 
   window.addEventListener('toolsFiltered', (event) => {
-    const currentView = document.querySelector('.view-toggle.active')?.getAttribute('data-view');
-    if (currentView === 'matrix') {
-      setTimeout(updateMatrixHighlighting, 50);
-    }
-  });
-
-  window.addEventListener('toolsFiltered', (event) => {
-    const filtered = event.detail;
+    const { tools: filtered, semanticSearch } = event.detail;
     const currentView = document.querySelector('.view-toggle.active')?.getAttribute('data-view');
     
     if (currentView === 'matrix') {
@@ -793,13 +805,6 @@ domains.forEach((domain: any) => {
       const domainAgnosticPhaseIds = domainAgnosticSoftware.map(section => section.id);
       const isDomainAgnosticPhase = domainAgnosticPhaseIds.includes(selectedPhase);
       
-      domainAgnosticSoftware.forEach(sectionData => {
-        const section = document.getElementById(`domain-agnostic-section-${sectionData.id}`);
-        const container = document.getElementById(`domain-agnostic-tools-${sectionData.id}`);
-        
-        if (!section || !container) return;
-      });
-      
       if (!isDomainAgnosticPhase) {
         document.getElementById('dfir-matrix-section').style.display = 'block';
         
@@ -808,9 +813,7 @@ domains.forEach((domain: any) => {
         });
         
         filtered.forEach(tool => {
-          if (tool.type === 'concept') {
-            return;
-          }
+          if (tool.type === 'concept') return;
           
           const isMethod = tool.type === 'method';
           const hasValidProjectUrl = window.isToolHosted(tool);
@@ -827,6 +830,7 @@ domains.forEach((domain: any) => {
                                 hasValidProjectUrl ? 'tool-chip-hosted' : 
                                 tool.license !== 'Proprietary' ? 'tool-chip-oss' : '';
                 chip.className = `tool-chip ${chipClass}`;
+                chip.setAttribute('data-tool-name', tool.name);
                 chip.setAttribute('title', `${tool.name}${tool.knowledgebase === true ? ' (KB verfügbar)' : ''}`);
                 chip.innerHTML = `${tool.name}${tool.knowledgebase === true ? '<span style="margin-left: 0.25rem; font-size: 0.6875rem;">📖</span>' : ''}`;
                 chip.onclick = () => window.showToolDetails(tool.name);
diff --git a/src/config/prompts.ts b/src/config/prompts.ts
index 86467b5..8802cd1 100644
--- a/src/config/prompts.ts
+++ b/src/config/prompts.ts
@@ -1,232 +1,307 @@
-// src/config/prompts.ts - Centralized German prompts for AI pipeline
+// src/config/prompts.ts - Enhanced with phase completion reasoning
 
 export const AI_PROMPTS = {
   
   toolSelection: (mode: string, userQuery: string, selectionMethod: string, maxSelectedItems: number) => {
     const modeInstruction = mode === 'workflow' 
-      ? 'Der Benutzer möchte einen UMFASSENDEN WORKFLOW mit mehreren Tools/Methoden über verschiedene Phasen. Wählen Sie 15-25 Tools aus, die den vollständigen Untersuchungslebenszyklus abdecken.'
-      : 'Der Benutzer möchte SPEZIFISCHE TOOLS/METHODEN, die ihr konkretes Problem direkt lösen. Wählen Sie 3-8 Tools aus, die am relevantesten und effektivsten sind.';
+      ? 'Workflow mit 15-25 Items über alle Phasen. PFLICHT: Mindestens 40% Methoden, Rest Tools/Konzepte.'
+      : 'Spezifische Lösung mit 4-10 Items. PFLICHT: Mindestens 30% Methoden wenn verfügbar.';
 
-    return `Sie sind ein DFIR-Experte mit Zugang zur kompletten forensischen Tool-Datenbank. Sie müssen die relevantesten Tools und Konzepte für diese spezifische Anfrage auswählen.
+    return `Du bist ein DFIR-Experte. Wähle die BESTEN Items aus dem vorgefilterten Set.
 
 AUSWAHLMETHODE: ${selectionMethod}
 ${selectionMethod === 'embeddings_candidates' ? 
-  'Diese Tools wurden durch Vektor-Ähnlichkeit vorgefiltert, sie sind bereits relevant. Ihre Aufgabe ist es, die BESTEN aus diesem relevanten Set auszuwählen.' :
-  'Sie haben Zugang zur vollständigen Tool-Datenbank. Wählen Sie die relevantesten Tools für die Anfrage aus.'}
+  '✓ Semantisch relevante Items bereits vorgefiltert\n✓ Wähle die BESTEN für die konkrete Aufgabe' :
+  '✓ Vollständige Datenbank verfügbar\n✓ Wähle die relevantesten Items'}
 
 ${modeInstruction}
 
-BENUTZER-ANFRAGE: "${userQuery}"
+ANFRAGE: "${userQuery}"
 
-KRITISCHE AUSWAHLPRINZIPIEN:
-1. **KONTEXT ÜBER POPULARITÄT**: Verwenden Sie nicht automatisch "berühmte" Tools wie Volatility, Wireshark oder Autopsy nur weil sie bekannt sind. Wählen Sie basierend auf den SPEZIFISCHEN Szenario-Anforderungen.
+VERFÜGBARE ITEM-TYPEN:
+- TOOLS (type: "software"/"method") → praktische Anwendungen und Vorgehensweisen  
+- KONZEPTE (type: "concept") → theoretisches Wissen und Methodiken
 
-2. **METHODOLOGIE vs SOFTWARE**: 
-   - Für SCHNELLE/DRINGENDE Szenarien → Priorisieren Sie METHODEN und schnelle Antwort-Ansätze
-   - Für ZEITKRITISCHE Vorfälle → Wählen Sie Triage-Methoden über tiefe Analyse-Tools
-   - Für UMFASSENDE Analysen → Dann betrachten Sie detaillierte Software-Tools
-   - METHODEN (Typ: "method") sind oft besser als SOFTWARE für prozedurale Anleitung
+AUSWAHLSTRATEGIE:
+1. **ERSTE PRIORITÄT: Relevanz zur Anfrage**
+  - Direkt anwendbar auf das Problem
+  - Löst die Kernherausforderung
 
-3. **SZENARIO-SPEZIFISCHE LOGIK**:
-   - "Schnell/Quick/Dringend/Triage" Szenarien → Rapid Incident Response und Triage METHODE > Volatility
-   - "Industrial/SCADA/ICS" Szenarien → Spezialisierte ICS-Tools > generische Netzwerk-Tools
-   - "Mobile/Android/iOS" Szenarien → Mobile-spezifische Tools > Desktop-Forensik-Tools
-   - "Speicher-Analyse dringend benötigt" → Schnelle Speicher-Tools/Methoden > umfassende Volatility-Analyse
+2. **ZWEITE PRIORITÄT: Ausgewogene Mischung**
+  - Tools/Methoden für praktische Umsetzung → selectedTools
+  - Konzepte für methodisches Verständnis → selectedConcepts
+  - WICHTIG: Auch Konzepte auswählen, nicht nur Tools!
 
-ANALYSE-ANWEISUNGEN:
-1. Lesen Sie die VOLLSTÄNDIGE Beschreibung jedes Tools/Konzepts
-2. Berücksichtigen Sie ALLE Tags, Plattformen, verwandte Tools und Metadaten
-3. **PASSENDE DRINGLICHKEIT**: Schnelle Szenarien brauchen schnelle Methoden, nicht tiefe Analyse-Tools
-4. **PASSENDE SPEZIFITÄT**: Spezialisierte Szenarien brauchen spezialisierte Tools, nicht generische
-5. **BERÜCKSICHTIGEN SIE DEN TYP**: Methoden bieten prozedurale Anleitung, Software bietet technische Fähigkeiten
+3. **QUALITÄT > QUANTITÄT**
+  - Lieber weniger perfekte Items als viele mittelmäßige
+  - Jedes Item muss begründbar sein
 
-Wählen Sie die relevantesten Elemente aus (max ${maxSelectedItems} insgesamt).
+AUSWAHLREGELN:
+- Wähle ${mode === 'workflow' ? '15-25' : '4-10'} Items total, max ${maxSelectedItems}
+- BEIDE Arrays füllen: selectedTools UND selectedConcepts
+- Mindestens 1-2 Konzepte auswählen für methodische Fundierung
+- Tools: 40% Methoden (type="method"), Rest Software (type="software")
 
-Antworten Sie NUR mit diesem JSON-Format:
+ANTWORT AUSSCHLIESSLICH IM JSON-FORMAT:
 {
-  "selectedTools": ["Tool Name 1", "Tool Name 2", ...],
-  "selectedConcepts": ["Konzept Name 1", "Konzept Name 2", ...],
-  "reasoning": "Detaillierte Erklärung, warum diese spezifischen Tools für diese Anfrage ausgewählt wurden, und warum bestimmte populäre Tools NICHT ausgewählt wurden, falls sie für den Szenario-Kontext ungeeignet waren"
+  "selectedTools": ["ToolName1", "MethodName1", ...],
+  "selectedConcepts": ["ConceptName1", "ConceptName2", ...],
+  "reasoning": "Kurze Begründung mit Erwähnung der Tool/Konzept-Balance"
 }`;
   },
 
+  toolSelectionWithData: (basePrompt: string, toolsToSend: any[], conceptsToSend: any[]) => {
+    return `${basePrompt}
+
+VERFÜGBARE TOOLS (${toolsToSend.length} Items - Methoden und Software):
+${JSON.stringify(toolsToSend, null, 2)}
+
+VERFÜGBARE KONZEPTE (${conceptsToSend.length} Items - theoretisches Wissen):
+${JSON.stringify(conceptsToSend, null, 2)}
+
+WICHTIGER HINWEIS: Wähle sowohl aus TOOLS als auch aus KONZEPTEN aus! Konzepte sind essentiell für methodische Fundierung.`;
+  },
+
   scenarioAnalysis: (isWorkflow: boolean, userQuery: string) => {
-    const analysisType = isWorkflow ? 'forensische Szenario' : 'technische Problem';
-    const considerations = isWorkflow ? 
-      `- Angriffsvektoren und Bedrohungsmodellierung nach MITRE ATT&CK
-- Betroffene Systeme und kritische Infrastrukturen
-- Zeitkritische Faktoren und Beweiserhaltung
-- Forensische Artefakte und Datenquellen` :
-      `- Spezifische forensische Herausforderungen
-- Verfügbare Datenquellen und deren Integrität
-- Methodische Anforderungen für rechtssichere Analyse`;
+    const analysisType = isWorkflow ? 'Szenario' : 'Problem';
+    const focus = isWorkflow ? 
+      'Angriffsvektoren, betroffene Systeme, Zeitkritikalität' :
+      'Kernherausforderung, verfügbare Daten, methodische Anforderungen';
 
-    return `Sie sind ein erfahrener DFIR-Experte. Analysieren Sie das folgende ${analysisType}.
+    return `DFIR-Experte: Analysiere das ${analysisType}.
 
-${isWorkflow ? 'FORENSISCHES SZENARIO' : 'TECHNISCHES PROBLEM'}: "${userQuery}"
+${isWorkflow ? 'SZENARIO' : 'PROBLEM'}: "${userQuery}"
 
-Führen Sie eine systematische ${isWorkflow ? 'Szenario-Analyse' : 'Problem-Analyse'} durch und berücksichtigen Sie dabei:
+Fokus: ${focus}
 
-${considerations}
-
-WICHTIG: Antworten Sie NUR in fließendem deutschen Text ohne Listen, Aufzählungen oder Markdown-Formatierung. Maximum 150 Wörter.`;
+Antwort: Fließtext ohne Listen, max 100 Wörter.`;
   },
 
   investigationApproach: (isWorkflow: boolean, userQuery: string) => {
     const approachType = isWorkflow ? 'Untersuchungsansatz' : 'Lösungsansatz';
-    const considerations = isWorkflow ?
-      `- Triage-Prioritäten nach forensischer Dringlichkeit
-- Phasenabfolge nach NIST-Methodik
-- Kontaminationsvermeidung und forensische Isolierung` :
-      `- Methodik-Auswahl nach wissenschaftlichen Kriterien
-- Validierung und Verifizierung der gewählten Ansätze
-- Integration in bestehende forensische Workflows`;
+    const focus = isWorkflow ?
+      'Triage-Prioritäten, Phasenabfolge, Kontaminationsvermeidung' :
+      'Methodenauswahl, Validierung, Integration';
 
-    return `Basierend auf der Analyse entwickeln Sie einen fundierten ${approachType} nach NIST SP 800-86 Methodik.
+    return `Entwickle einen ${approachType}.
 
 ${isWorkflow ? 'SZENARIO' : 'PROBLEM'}: "${userQuery}"
 
-Entwickeln Sie einen systematischen ${approachType} unter Berücksichtigung von:
+Fokus: ${focus}
 
-${considerations}
-
-WICHTIG: Antworten Sie NUR in fließendem deutschen Text ohne Listen oder Markdown. Maximum 150 Wörter.`;
+Antwort: Fließtext ohne Listen, max 100 Wörter.`;
   },
 
   criticalConsiderations: (isWorkflow: boolean, userQuery: string) => {
-    const considerationType = isWorkflow ? 'kritische forensische Überlegungen' : 'wichtige methodische Voraussetzungen';
-    const aspects = isWorkflow ?
-      `- Time-sensitive evidence preservation
-- Chain of custody requirements und rechtliche Verwertbarkeit
-- Incident containment vs. evidence preservation Dilemma
-- Privacy- und Compliance-Anforderungen` :
-      `- Tool-Validierung und Nachvollziehbarkeit
-- False positive/negative Risiken bei der gewählten Methodik
-- Qualifikationsanforderungen für die Durchführung
-- Dokumentations- und Reporting-Standards`;
+    const focus = isWorkflow ? 
+      'Beweissicherung vs. Gründlichkeit, Chain of Custody' : 
+      'Tool-Validierung, False Positives/Negatives, Qualifikationen';
 
-    return `Identifizieren Sie ${considerationType} für diesen Fall.
+    return `Identifiziere kritische Überlegungen.
 
 ${isWorkflow ? 'SZENARIO' : 'PROBLEM'}: "${userQuery}"
 
-Berücksichtigen Sie folgende forensische Aspekte:
+Fokus: ${focus}
 
-${aspects}
-
-WICHTIG: Antworten Sie NUR in fließendem deutschen Text ohne Listen oder Markdown. Maximum 120 Wörter.`;
+Antwort: Fließtext ohne Listen, max 100 Wörter.`;
   },
 
   phaseToolSelection: (userQuery: string, phase: any, phaseTools: any[]) => {
-    return `Wählen Sie 2-3 Methoden/Tools für die Phase "${phase.name}" und bewerten Sie deren Aufgaben-Eignung VERGLEICHEND.
+    const methods = phaseTools.filter(t => t.type === 'method');
+    const tools = phaseTools.filter(t => t.type === 'software');
+    
+    if (phaseTools.length === 0) {
+      return `Keine Methoden/Tools für Phase "${phase.name}" verfügbar. Antworte mit leerem Array: []`;
+    }
+    
+    return `Du bist ein DFIR-Experte. Wähle die 2-3 BESTEN Items für Phase "${phase.name}".
 
 SZENARIO: "${userQuery}"
-SPEZIFISCHE PHASE: ${phase.name} - ${phase.description || 'Forensische Untersuchungsphase'}
+PHASE: ${phase.name} - ${phase.description || ''}
 
-VERFÜGBARE TOOLS FÜR ${phase.name.toUpperCase()}:
-${phaseTools.map((tool: any, index: number) => `${index + 1}. ${tool.name}: ${tool.description.slice(0, 150)}...
-  - Plattformen: ${tool.platforms?.join(', ') || 'N/A'}
-  - Skill Level: ${tool.skillLevel}
-  - Tags: ${tool.tags?.join(', ') || 'N/A'}`).join('\n\n')}
+VERFÜGBARE ITEMS (bereits von KI vorausgewählt):
+${methods.length > 0 ? `
+METHODEN (${methods.length}):
+${methods.map((method: any) => 
+  `- ${method.name}
+  Typ: ${method.type}
+  Beschreibung: ${method.description}
+  Domains: ${method.domains?.join(', ') || 'N/A'}
+  Skill Level: ${method.skillLevel}`
+).join('\n\n')}
+` : 'Keine Methoden verfügbar'}
 
-Bewerten Sie ALLE Tools vergleichend für diese spezifische Aufgabe UND Phase. Wählen Sie die 2-3 besten aus.
+${tools.length > 0 ? `
+SOFTWARE TOOLS (${tools.length}):
+${tools.map((tool: any) => 
+  `- ${tool.name}
+  Typ: ${tool.type}
+  Beschreibung: ${tool.description}
+  Plattformen: ${tool.platforms?.join(', ') || 'N/A'}
+  Skill Level: ${tool.skillLevel}`
+).join('\n\n')}
+` : 'Keine Software-Tools verfügbar'}
 
-BEWERTUNGSKRITERIEN:
-- Wie gut löst das Tool das forensische Problem im SZENARIO-Kontext?
-- Wie gut passt es zur spezifischen PHASE "${phase.name}"?
-- Wie vergleicht es sich mit den anderen verfügbaren Tools für diese Phase?
+AUSWAHLREGELN FÜR PHASE "${phase.name}":
+1. Wähle die 2-3 BESTEN Items für diese spezifische Phase
+2. Priorisiere Items, die DIREKT für "${phase.name}" relevant sind
+3. Mindestens 1 Methode wenn verfügbar, Rest Software-Tools
+4. Begründe WARUM jedes Item für diese Phase optimal ist
 
-Antworten Sie AUSSCHLIESSLICH mit diesem JSON-Format:
+WICHTIG: Verwende EXAKT die Namen wie oben aufgelistet (ohne Präfixe wie M1./T2.)!
+
+ANTWORT AUSSCHLIESSLICH IM JSON-FORMAT OHNE JEGLICHEN TEXT AUSSERHALB:
 [
   {
-    "toolName": "Exakter Tool-Name",
+    "toolName": "Exakter Name aus der Liste oben",
     "taskRelevance": 85,
-    "justification": "Vergleichende Begründung warum dieses Tool für diese Phase und Aufgabe besser/schlechter als die anderen geeignet ist",
-    "limitations": ["Spezifische Einschränkung 1", "Einschränkung 2"]
-  }
-]
-
-WICHTIG:
-- taskRelevance: 0-100 Score basierend auf Szenario-Eignung UND Phasen-Passung im VERGLEICH zu anderen Tools
-- Nur die 2-3 BESTEN Tools auswählen und bewerten
-- justification soll VERGLEICHEND sein ("besser als X weil...", "für diese Phase ideal weil...")`;
-  },
-
-  toolEvaluation: (userQuery: string, tool: any, rank: number, taskRelevance: number) => {
-    return `Sie sind ein DFIR-Experte. Erklären Sie DETAILLIERT die Anwendung dieses bereits bewerteten Tools.
-
-PROBLEM: "${userQuery}"
-TOOL: ${tool.name} (bereits bewertet mit ${taskRelevance}% Aufgaben-Eignung)
-BESCHREIBUNG: ${tool.description}
-
-Das Tool wurde bereits als Rang ${rank} für diese Aufgabe bewertet. Erklären Sie nun:
-
-Antworten Sie AUSSCHLIESSLICH mit diesem JSON-Format:
-{
-  "detailed_explanation": "Detaillierte Erklärung warum und wie dieses Tool für diese spezifische Aufgabe eingesetzt wird",
-  "implementation_approach": "Konkrete Schritt-für-Schritt Anleitung zur korrekten Anwendung",
-  "pros": ["Spezifischer Vorteil 1", "Spezifischer Vorteil 2"],
-  "limitations": ["Spezifische Einschränkung 1", "Spezifische Einschränkung 2"],
-  "alternatives": "Alternative Ansätze oder Tools falls dieses nicht verfügbar ist"
-}
-
-WICHTIG: 
-- Keine erneute Bewertung - nur detaillierte Erklärung der bereits bewerteten Eignung
-- "limitations" soll spezifische technische/methodische Einschränkungen des Tools auflisten
-- "pros" soll die Stärken für diese spezifische Aufgabe hervorheben`;
-  },
-
-  backgroundKnowledgeSelection: (userQuery: string, mode: string, selectedToolNames: string[], availableConcepts: any[]) => {
-    return `Wählen Sie relevante forensische Konzepte für das Verständnis der empfohlenen Methodik.
-
-${mode === 'workflow' ? 'SZENARIO' : 'PROBLEM'}: "${userQuery}"
-EMPFOHLENE TOOLS: ${selectedToolNames.join(', ')}
-
-VERFÜGBARE KONZEPTE:
-${availableConcepts.slice(0, 15).map((concept: any) => `- ${concept.name}: ${concept.description.slice(0, 80)}...`).join('\n')}
-
-Wählen Sie 2-4 Konzepte aus, die für das Verständnis der forensischen Methodik essentiell sind.
-
-Antworten Sie AUSSCHLIESSLICH mit diesem JSON-Format:
-[
-  {
-    "conceptName": "Exakter Konzept-Name",
-    "relevance": "Forensische Relevanz: Warum dieses Konzept für das Verständnis der Methodik kritisch ist"
+    "justification": "Detaillierte Begründung (60-80 Wörter) warum optimal für ${phase.name} - erkläre Anwendung, Vorteile und spezifische Relevanz",
+    "limitations": ["Mögliche Einschränkung für diese Phase"]
   }
 ]`;
   },
 
-  finalRecommendations: (isWorkflow: boolean, userQuery: string, selectedToolNames: string[]) => {
-    const prompt = isWorkflow ? 
-      `Erstellen Sie eine Workflow-Empfehlung basierend auf DFIR-Prinzipien.
-
-SZENARIO: "${userQuery}"
-AUSGEWÄHLTE TOOLS: ${selectedToolNames.join(', ') || 'Keine Tools ausgewählt'}
-
-Erstellen Sie konkrete methodische Workflow-Schritte für dieses spezifische Szenario unter Berücksichtigung forensischer Best Practices, Objektivität und rechtlicher Verwertbarkeit.
-
-WICHTIG: Antworten Sie NUR in fließendem deutschen Text ohne Listen oder Markdown. Maximum 120 Wörter.` :
-      
-      `Erstellen Sie wichtige methodische Überlegungen für die korrekte Methoden-/Tool-Anwendung.
+  toolEvaluation: (userQuery: string, tool: any, rank: number, taskRelevance: number) => {
+    const itemType = tool.type === 'method' ? 'Methode' : 'Tool';
+    
+    return `Erkläre die Anwendung dieser/dieses ${itemType}.
 
 PROBLEM: "${userQuery}"
-EMPFOHLENE TOOLS: ${selectedToolNames.join(', ') || 'Keine Methoden/Tools ausgewählt'}
+${itemType.toUpperCase()}: ${tool.name} (${taskRelevance}% Eignung)
+TYP: ${tool.type}
 
-Geben Sie kritische methodische Überlegungen, Validierungsanforderungen und Qualitätssicherungsmaßnahmen für die korrekte Anwendung der empfohlenen Methoden/Tools.
+Bereits als Rang ${rank} bewertet.
 
-WICHTIG: Antworten Sie NUR in fließendem deutschen Text ohne Listen oder Markdown. Maximum 100 Wörter.`;
+ANTWORT AUSSCHLIESSLICH IM JSON-FORMAT OHNE JEGLICHEN TEXT AUSSERHALB DER JSON-STRUKTUR:
+{
+  "detailed_explanation": "Warum und wie einsetzen",
+  "implementation_approach": "Konkrete Schritte",
+  "pros": ["Vorteil 1", "Vorteil 2"],
+  "limitations": ["Einschränkung 1"],
+  "alternatives": "Alternative Ansätze"
+}`;
+  },
 
-    return prompt;
+  backgroundKnowledgeSelection: (userQuery: string, mode: string, selectedToolNames: string[], availableConcepts: any[]) => {
+    return `Wähle 2-4 relevante Konzepte.
+
+${mode === 'workflow' ? 'SZENARIO' : 'PROBLEM'}: "${userQuery}"
+AUSGEWÄHLTE TOOLS: ${selectedToolNames.join(', ')}
+
+VERFÜGBARE KONZEPTE (${availableConcepts.length} KI-kuratiert):
+${availableConcepts.map((c: any) => 
+  `- ${c.name}: ${c.description}...`
+).join('\n')}
+
+ANTWORT AUSSCHLIESSLICH IM JSON-FORMAT OHNE JEGLICHEN TEXT AUSSERHALB DER JSON-STRUKTUR:
+[
+  {
+    "conceptName": "Name",
+    "relevance": "Warum kritisch für Methodik"
+  }
+]`;
+  },
+
+  phaseCompletionReasoning: (
+    originalQuery: string,
+    phase: any,
+    selectedToolName: string,
+    tool: any,
+    completionContext: string
+  ) => {
+    return `Du bist ein DFIR-Experte. Erkläre warum dieses Tool nachträglich zur Vervollständigung hinzugefügt wurde.
+
+KONTEXT DER NACHTRÄGLICHEN ERGÄNZUNG:
+- Ursprüngliche KI-Auswahl war zu spezifisch/eng gefasst
+- Phase "${phase.name}" war unterrepräsentiert in der initialen Auswahl
+- Semantische Suche fand zusätzlich relevante Tools für diese Phase
+- Tool wird nachträglich hinzugefügt um Vollständigkeit zu gewährleisten
+
+URSPRÜNGLICHE ANFRAGE: "${originalQuery}"
+PHASE ZU VERVOLLSTÄNDIGEN: ${phase.name} - ${phase.description || ''}
+HINZUGEFÜGTES TOOL: ${selectedToolName} (${tool.type})
+TOOL-BESCHREIBUNG: ${tool.description}
+
+BEGRÜNDUNGSKONTEXT: ${completionContext}
+
+Erstelle eine präzise Begründung (max. 40 Wörter), die erklärt:
+1. WARUM dieses Tool nachträglich hinzugefügt wurde
+2. WIE es die ${phase.name}-Phase ergänzt
+3. DASS es die ursprünglich zu spezifische Auswahl erweitert
+
+Antwort: Prägnanter Fließtext, knappe Begründung für Nachergänzung. Vermeide Begriffe wie "Das Tool" und gib keinen einleitenden Text wie "Begründung (40 Wörter):" an.`;
+  },
+
+  generatePhaseCompletionPrompt(
+    originalQuery: string,
+    phase: any,
+    candidateTools: any[],
+    candidateConcepts: any[]
+  ): string {
+    return `Du bist ein DFIR-Experte. Die initiale KI-Auswahl war zu spezifisch - die Phase "${phase.name}" ist unterrepräsentiert.
+
+KONTEXT: Die Hauptauswahl hat zu wenige Tools für "${phase.name}" identifiziert. Wähle jetzt ergänzende Tools aus semantischer Nachsuche.
+
+ORIGINAL ANFRAGE: "${originalQuery}"
+UNTERREPRÄSENTIERTE PHASE: ${phase.name} - ${phase.description || ''}
+
+SEMANTISCH GEFUNDENE KANDIDATEN für Nachergänzung:
+
+VERFÜGBARE TOOLS (${candidateTools.length}):
+${candidateTools.map((tool: any) => `
+- ${tool.name} (${tool.type})
+  Beschreibung: ${tool.description}
+  Skill Level: ${tool.skillLevel}
+`).join('')}
+
+${candidateConcepts.length > 0 ? `
+VERFÜGBARE KONZEPTE (${candidateConcepts.length}):
+${candidateConcepts.map((concept: any) => `
+- ${concept.name}
+  Beschreibung: ${concept.description}
+`).join('')}
+` : ''}
+
+AUSWAHLREGELN FÜR NACHERGÄNZUNG:
+1. Wähle 1-2 BESTE Methoden/Tools die die ${phase.name}-Phase optimal ergänzen
+2. Methoden/Tools müssen für die ursprüngliche Anfrage relevant sein
+3. Ergänzen, nicht ersetzen - erweitere die zu spezifische Erstauswahl
+
+ANTWORT AUSSCHLIESSLICH IM JSON-FORMAT:
+{
+  "selectedTools": ["ToolName1", "ToolName2"],
+  "selectedConcepts": ["ConceptName1"],
+  "completionReasoning": "Kurze Erklärung warum diese Nachergänzung für ${phase.name} notwendig war"
+}`;
+  },
+
+  finalRecommendations: (isWorkflow: boolean, userQuery: string, selectedToolNames: string[]) => {
+    const focus = isWorkflow ? 
+      'Workflow-Schritte, Best Practices, Objektivität' :
+      'Methodische Überlegungen, Validierung, Qualitätssicherung';
+
+    return `Erstelle ${isWorkflow ? 'Workflow-Empfehlung' : 'methodische Überlegungen'}.
+
+${isWorkflow ? 'SZENARIO' : 'PROBLEM'}: "${userQuery}"
+AUSGEWÄHLT: ${selectedToolNames.join(', ')}${selectedToolNames.length > 5 ? '...' : ''}
+
+Fokus: ${focus}
+
+Antwort: Fließtext ohne Listen, max ${isWorkflow ? '100' : '80'} Wörter.`;
   }
 } as const;
 
 export function getPrompt(key: 'toolSelection', mode: string, userQuery: string, selectionMethod: string, maxSelectedItems: number): string;
+export function getPrompt(key: 'toolSelectionWithData', basePrompt: string, toolsToSend: any[], conceptsToSend: any[]): string;
 export function getPrompt(key: 'scenarioAnalysis', isWorkflow: boolean, userQuery: string): string;
 export function getPrompt(key: 'investigationApproach', isWorkflow: boolean, userQuery: string): string;
 export function getPrompt(key: 'criticalConsiderations', isWorkflow: boolean, userQuery: string): string;
 export function getPrompt(key: 'phaseToolSelection', userQuery: string, phase: any, phaseTools: any[]): string;
 export function getPrompt(key: 'toolEvaluation', userQuery: string, tool: any, rank: number, taskRelevance: number): string;
 export function getPrompt(key: 'backgroundKnowledgeSelection', userQuery: string, mode: string, selectedToolNames: string[], availableConcepts: any[]): string;
+export function getPrompt(key: 'phaseCompletionReasoning', originalQuery: string, phase: any, selectedToolName: string, tool: any, completionContext: string): string;
 export function getPrompt(key: 'finalRecommendations', isWorkflow: boolean, userQuery: string, selectedToolNames: string[]): string;
+export function getPrompt(key: 'generatePhaseCompletionPrompt', originalQuery: string, phase: any, candidateTools: any[], candidateConcepts: any[]): string;
 export function getPrompt(promptKey: keyof typeof AI_PROMPTS, ...args: any[]): string {
   try {
     const promptFunction = AI_PROMPTS[promptKey];
diff --git a/src/content/knowledgebase/concept-digital-evidence-chain.md b/src/content/knowledgebase/concept-digital-evidence-chain.md
new file mode 100644
index 0000000..3bee49d
--- /dev/null
+++ b/src/content/knowledgebase/concept-digital-evidence-chain.md
@@ -0,0 +1,616 @@
+---
+title: "Digital Evidence Chain of Custody: Lückenlose Beweisführung in der digitalen Forensik"
+description: "Umfassender Leitfaden für die rechtssichere Dokumentation digitaler Beweise von der Sicherstellung bis zur Gerichtsverhandlung. Praktische Umsetzung von ISO 27037, Dokumentationsstandards und häufige Fallstricke."
+author: "Claude 4 Sonnett (Prompt: Mario Stöckl)"
+last_updated: 2025-08-10
+difficulty: advanced
+categories: ["standards", "documentation", "legal-compliance", "case-management"]
+tags: ["chain-of-custody", "iso-27037", "court-admissible", "audit-trail", "hash-verification", "tamper-evidence", "legal-compliance", "documentation", "process-management", "evidence-handling"]
+published: true
+---
+
+# Digital Evidence Chain of Custody: Lückenlose Beweisführung in der digitalen Forensik
+
+Die **Chain of Custody** (Beweiskette) ist das Rückgrat jeder forensischen Untersuchung und entscheidet oft über Erfolg oder Misserfolg vor Gericht. Dieser Leitfaden erklärt die rechtssicheren Verfahren für die lückenlose Dokumentation digitaler Beweise von der Sicherstellung bis zur Gerichtsverhandlung.
+
+## Warum ist die Chain of Custody entscheidend?
+
+In der digitalen Forensik können Beweise innerhalb von Sekunden manipuliert, gelöscht oder verfälscht werden. Eine ordnungsgemäße Chain of Custody gewährleistet:
+
+- **Gerichtliche Verwertbarkeit** der Beweise
+- **Nachweis der Authentizität** und Integrität
+- **Schutz vor Manipulationsvorwürfen**
+- **Rechtssicherheit** für alle Beteiligten
+- **Compliance** mit internationalen Standards
+
+> **Warnung**: Bereits kleine Fehler in der Beweiskette können zur kompletten Verwerfung der Beweise führen und jahrelange Ermittlungsarbeit zunichte machen.
+
+## Rechtliche Grundlagen und Standards
+
+### Internationale Standards
+
+**ISO/IEC 27037:2012** - "Guidelines for identification, collection, acquisition and preservation of digital evidence"
+- Definiert Best Practices für digitale Beweismittel
+- International anerkannter Standard
+- Basis für nationale Implementierungen
+
+**ISO/IEC 27041:2015** - "Guidance on assuring suitability and adequacy of incident investigative method"
+- Ergänzt ISO 27037 um Qualitätssicherung
+- Fokus auf Angemessenheit der Methoden
+
+### Nationale Rahmenwerke
+
+**Deutschland**:
+- § 81a StPO (Körperliche Untersuchung)
+- § 94 ff. StPO (Beschlagnahme)
+- BSI-Standards zur IT-Forensik
+
+**USA**:
+- Federal Rules of Evidence (Rule 901, 902)
+- NIST Special Publication 800-86
+
+**EU**:
+- GDPR-Compliance bei der Beweissicherung
+- eIDAS-Verordnung für digitale Signaturen
+
+## Die vier Säulen der Chain of Custody
+
+### 1. Authentizität (Echtheit)
+**Definition**: Nachweis, dass die Beweise tatsächlich von der behaupteten Quelle stammen.
+
+**Praktische Umsetzung**:
+```bash
+# Cryptographic Hash Generation
+sha256sum /dev/sdb1 > evidence_hash.txt
+md5sum /dev/sdb1 >> evidence_hash.txt
+
+# Mit Zeitstempel
+echo "$(date -u +%Y-%m-%dT%H:%M:%SZ): $(sha256sum /dev/sdb1)" >> chain_log.txt
+```
+
+### 2. Integrität (Unversehrtheit)
+**Definition**: Sicherstellung, dass die Beweise seit der Sicherstellung unverändert geblieben sind.
+
+**Maßnahmen**:
+- **Write-Blocker** bei allen Zugriffen
+- **Hash-Verifizierung** vor und nach jeder Bearbeitung
+- **Versionskontrolle** für alle Arbeitskopien
+
+### 3. Nachvollziehbarkeit (Traceability)
+**Definition**: Lückenlose Dokumentation aller Personen, die Zugang zu den Beweisen hatten.
+
+**Dokumentationspflicht**: Wer, Was, Wann, Wo, Warum
+
+### 4. Nicht-Abstreitbarkeit (Non-Repudiation)
+**Definition**: Verhinderung, dass Beteiligte ihre Handlungen später abstreiten können.
+
+**Technische Lösung**: Digitale Signaturen, Blockchain-Timestamping
+
+## Praktische Implementierung: Schritt-für-Schritt
+
+### Phase 1: Vorbereitung der Sicherstellung
+
+**Equipment-Check**:
+```checklist
+□ Kalibrierte Write-Blocker
+□ Forensische Imaging-Tools
+□ Chain of Custody Formulare
+□ Tamper-evident Bags/Labels
+□ Digitalkamera für Dokumentation
+□ Messgeräte (falls erforderlich)
+□ Backup-Ausrüstung
+```
+
+**Dokumentation vor Ort**:
+1. **Umgebungsfotografie** (360°-Dokumentation)
+2. **Hardware-Identifikation** (Seriennummern, Labels)
+3. **Netzwerkzustand** (aktive Verbindungen)
+4. **Bildschirmzustand** (Screenshots vor Herunterfahren)
+
+### Phase 2: Sichere Akquisition
+
+**Write-Blocker Setup**:
+```bash
+# Hardware Write-Blocker Verification
+lsblk -o NAME,SIZE,RO,TYPE,MOUNTPOINT
+# RO sollte "1" anzeigen für geschützte Devices
+
+# Software Write-Blocker (Linux)
+blockdev --setro /dev/sdb
+blockdev --getro /dev/sdb  # Should return 1
+```
+
+**Imaging mit Integrity Check**:
+```bash
+# dd mit Hash-Berechnung
+dd if=/dev/sdb | tee >(sha256sum > image.sha256) | dd of=evidence.dd
+
+# Oder mit dcfldd für bessere Forensik-Features
+dcfldd if=/dev/sdb of=evidence.dd hash=sha256,md5 hashlog=hashlog.txt bs=4096
+```
+
+### Phase 3: Dokumentation und Versiegelung
+
+**Chain of Custody Form - Kernelemente**:
+
+```
+DIGITAL EVIDENCE CUSTODY FORM
+
+Fall-ID: _______________  Datum: _______________
+Ermittler: _______________  Badge/ID: _______________
+
+BEWEISMITTEL DETAILS:
+- Beschreibung: ________________________________
+- Seriennummer: _______________________________
+- Hersteller/Modell: ___________________________
+- Kapazität: __________________________________
+- Hash-Werte:
+  * SHA256: ___________________________________
+  * MD5: _____________________________________
+
+CUSTODY CHAIN:
+[Datum/Zeit] [Übernommen von] [Übergeben an] [Zweck] [Unterschrift]
+_________________________________________________________________
+_________________________________________________________________
+
+INTEGRITÄT BESTÄTIGT:
+□ Write-Blocker verwendet
+□ Hash-Werte verifiziert  
+□ Tamper-evident versiegelt
+□ Fotos dokumentiert
+```
+
+**Versiegelung**:
+```
+Tamper-Evident Label Nummer: ______________
+Siegeltyp: _______________________________
+Platzierung: _____________________________
+Foto-Referenz: ___________________________
+```
+
+### Phase 4: Transport und Lagerung
+
+**Sichere Aufbewahrung**:
+- **Klimakontrollierte Umgebung** (15-25°C, <60% Luftfeuchtigkeit)
+- **Elektromagnetische Abschirmung** (Faraday-Käfig)
+- **Zugangskontrolle** (Biometrie, Kartenleser)
+- **Überwachung** (24/7 Video, Alarme)
+
+**Transport-Protokoll**:
+```
+TRANSPORT LOG
+
+Von: ______________________ Nach: ______________________
+Datum/Zeit Start: _____________ Ankunft: _______________
+Transportmittel: ___________________________________
+Begleitpersonen: ___________________________________
+Spezielle Vorkehrungen: ____________________________
+
+Integrität bei Ankunft:
+□ Siegel unversehrt
+□ Hash-Werte überprüft
+□ Keine physischen Schäden
+□ Dokumentation vollständig
+
+Empfänger: _________________ Unterschrift: _____________
+```
+
+## Digitale Chain of Custody Tools
+
+### Laboratory Information Management Systems (LIMS)
+
+**Kommerzielle Lösungen**:
+- **FRED (Forensic Recovery of Evidence Device)**
+- **CaseGuard** von AccessData
+- **EnCase Legal** von OpenText
+
+**Open Source Alternativen**:
+```python
+# Beispiel: Python-basierte CoC Tracking
+import hashlib
+import datetime
+import json
+from cryptography.fernet import Fernet
+
+class ChainOfCustody:
+    def __init__(self):
+        self.evidence_log = []
+        self.key = Fernet.generate_key()
+        self.cipher = Fernet(self.key)
+    
+    def add_custody_event(self, evidence_id, handler, action, location):
+        event = {
+            'timestamp': datetime.datetime.utcnow().isoformat(),
+            'evidence_id': evidence_id,
+            'handler': handler,
+            'action': action,
+            'location': location,
+            'hash': self.calculate_hash(evidence_id)
+        }
+        
+        # Encrypt sensitive data
+        encrypted_event = self.cipher.encrypt(json.dumps(event).encode())
+        self.evidence_log.append(encrypted_event)
+        
+        return event
+    
+    def calculate_hash(self, evidence_path):
+        """Calculate SHA256 hash of evidence file"""
+        hash_sha256 = hashlib.sha256()
+        with open(evidence_path, "rb") as f:
+            for chunk in iter(lambda: f.read(4096), b""):
+                hash_sha256.update(chunk)
+        return hash_sha256.hexdigest()
+```
+
+### Blockchain-basierte Lösungen
+
+**Unveränderliche Timestamps**:
+```solidity
+// Ethereum Smart Contract Beispiel
+pragma solidity ^0.8.0;
+
+contract EvidenceChain {
+    struct CustodyEvent {
+        uint256 timestamp;
+        string evidenceId;
+        string handler;
+        string action;
+        string hashValue;
+    }
+    
+    mapping(string => CustodyEvent[]) public evidenceChain;
+    
+    event CustodyTransfer(
+        string indexed evidenceId,
+        string handler,
+        uint256 timestamp
+    );
+    
+    function addCustodyEvent(
+        string memory _evidenceId,
+        string memory _handler,
+        string memory _action,
+        string memory _hashValue
+    ) public {
+        evidenceChain[_evidenceId].push(CustodyEvent({
+            timestamp: block.timestamp,
+            evidenceId: _evidenceId,
+            handler: _handler,
+            action: _action,
+            hashValue: _hashValue
+        }));
+        
+        emit CustodyTransfer(_evidenceId, _handler, block.timestamp);
+    }
+}
+```
+
+## Häufige Fehler und Fallstricke
+
+### Kritische Dokumentationsfehler
+
+**1. Unvollständige Handler-Information**
+```
+❌ Falsch: "IT-Abteilung"
+✅ Richtig: "Max Mustermann, IT-Administrator, Badge #12345, Abteilung IT-Security"
+```
+
+**2. Unspezifische Aktionsbeschreibungen**
+```
+❌ Falsch: "Analyse durchgeführt"
+✅ Richtig: "Keyword-Suche nach 'vertraulich' mit EnCase v21.2, 
+           Read-Only Zugriff, Image Hash vor/nach verifiziert"
+```
+
+**3. Lückenhafte Zeiterfassung**
+```
+❌ Falsch: "15:30"
+✅ Richtig: "2024-01-15T15:30:27Z (UTC), Zeitzone CET+1"
+```
+
+### Technische Fallstricke
+
+**Hash-Algorithmus Schwächen**:
+```bash
+# Vermeide MD5 für neue Fälle (Kollisionsanfällig)
+❌ md5sum evidence.dd
+
+# Verwende stärkere Algorithmen
+✅ sha256sum evidence.dd
+✅ sha3-256sum evidence.dd  # Noch sicherer
+```
+
+**Write-Blocker Bypass**:
+```bash
+# Prüfe IMMER Write-Protection
+blockdev --getro /dev/sdb
+if [ $? -eq 0 ]; then
+    echo "Write protection AKTIV"
+else
+    echo "WARNUNG: Write protection NICHT aktiv!"
+    exit 1
+fi
+```
+
+### Rechtliche Fallstricke
+
+**GDPR-Compliance bei EU-Fällen**:
+- **Datenschutz-Folgenabschätzung** vor Imaging
+- **Zweckbindung** der Beweiserhebung
+- **Löschfristen** nach Verfahrensabschluss
+
+**Jurisdiktionsprobleme**:
+- **Cloud-Evidence** in verschiedenen Ländern
+- **Verschiedene Beweisstandards** (Common Law vs. Civil Law)
+- **Internationale Rechtshilfe** erforderlich
+
+## Qualitätssicherung und Audit
+
+### Peer Review Verfahren
+
+**4-Augen-Prinzip**:
+```
+Imaging-Protokoll:
+Techniker A: _________________ (Durchführung)
+Techniker B: _________________ (Verifikation)
+Supervisor: __________________ (Freigabe)
+```
+
+**Hash-Verifikation Zeitplan**:
+```
+Initial:     SHA256 bei Akquisition
+Transport:   Hash-Check vor/nach Transport  
+Labor:       Hash-Check bei Laborankunft
+Analyse:     Hash-Check vor jeder Analyse
+Archiv:      Hash-Check bei Archivierung
+Vernichtung: Final Hash-Check vor Vernichtung
+```
+
+### Continuous Monitoring
+
+**Automated Integrity Checks**:
+```bash
+#!/bin/bash
+# integrity_monitor.sh
+
+EVIDENCE_DIR="/secure/evidence"
+LOG_FILE="/var/log/evidence_integrity.log"
+
+for evidence_file in "$EVIDENCE_DIR"/*.dd; do
+    stored_hash=$(cat "${evidence_file}.sha256")
+    current_hash=$(sha256sum "$evidence_file" | cut -d' ' -f1)
+    
+    if [ "$stored_hash" != "$current_hash" ]; then
+        echo "ALERT: Integrity violation detected for $evidence_file" | \
+        tee -a "$LOG_FILE"
+        # Send immediate alert
+        mail -s "Evidence Integrity Alert" admin@forensics.org < \
+        "$LOG_FILE"
+    fi
+done
+```
+
+## Internationale Gerichtspraxis
+
+### Deutschland - BGH Rechtsprechung
+
+**BGH 1 StR 142/18** (2018):
+- Digitale Beweise müssen **nachvollziehbar erhoben** werden
+- **Hash-Werte allein** reichen nicht aus
+- **Gesamter Erhebungsprozess** muss dokumentiert sein
+
+### USA - Federal Courts
+
+**United States v. Tank (2018)**:
+- **Authentication** unter Federal Rule 901(b)(9)
+- **Best Practices** sind nicht immer **rechtlich erforderlich**
+- **Totality of circumstances** entscheidet
+
+### EU - EuGH Rechtsprechung
+
+**Rechtssache C-203/15** (2016):
+- **Grundrechte** vs. **Strafverfolgung**
+- **Verhältnismäßigkeit** der Beweiserhebung
+- **GDPR-Compliance** auch bei strafrechtlichen Ermittlungen
+
+## Fallstudien aus der Praxis
+
+### Case Study 1: Ransomware-Angriff Automobilhersteller
+
+**Szenario**: 
+Ransomware-Angriff auf Produktionssysteme, 50+ Systeme betroffen
+
+**CoC-Herausforderungen**:
+- **Zeitdruck** durch Produktionsstillstand
+- **Verschiedene Standorte** (Deutschland, Tschechien, Mexiko)
+- **Rechtliche Anforderungen** in 3 Jurisdiktionen
+
+**Lösung**:
+```
+Parallel Teams:
+- Team 1: Incident Response (Live-Analyse)
+- Team 2: Evidence Preservation (Imaging)
+- Team 3: Documentation (CoC-Protokoll)
+
+Zentrale Koordination:
+- Shared CoC-Database (Cloud-basiert)
+- Video-Calls für Custody-Transfers
+- Digital Signatures für Remote-Bestätigung
+```
+
+**Lessons Learned**:
+- **Vorab-Planung** für Multi-Jurisdiktion essentiell
+- **Remote-CoC-Verfahren** erforderlich
+- **24/7-Verfügbarkeit** der Dokumentationssysteme
+
+### Case Study 2: Betrugsermittlung Finanzdienstleister
+
+**Szenario**:
+Verdacht auf Insiderhandel, E-Mail-Analyse von 500+ Mitarbeitern
+
+**CoC-Komplexität**:
+- **Privacy Laws** (GDPR, Bankengeheimnis)
+- **Privileged Communications** (Anwalt-Mandant)
+- **Regulatory Oversight** (BaFin, SEC)
+
+**Chain of Custody Strategie**:
+```
+Segregated Processing:
+1. Initial Triage (Automated)
+2. Legal Review (Attorney-Client Privilege)
+3. Regulatory Notification (Compliance)
+4. Technical Analysis (Forensik-Team)
+
+Access Controls:
+- Role-based Evidence Access
+- Need-to-know Principle
+- Audit Log for every Access
+```
+
+## Technologie-Trends und Zukunftsausblick
+
+### KI-basierte CoC-Automatisierung
+
+**Machine Learning für Anomalie-Erkennung**:
+```python
+from sklearn.ensemble import IsolationForest
+import pandas as pd
+
+# CoC Event Anomaly Detection
+def detect_custody_anomalies(custody_events):
+    """
+    Detect unusual patterns in custody transfers
+    """
+    features = pd.DataFrame(custody_events)
+    
+    # Feature Engineering
+    features['time_delta'] = features['timestamp'].diff()
+    features['handler_changes'] = features['handler'].ne(features['handler'].shift())
+    
+    # Anomaly Detection
+    model = IsolationForest(contamination=0.1)
+    anomalies = model.fit_predict(features.select_dtypes(include=[np.number]))
+    
+    return features[anomalies == -1]
+```
+
+### Quantum-Safe Cryptography
+
+**Vorbereitung auf Post-Quantum Era**:
+```
+Current:     RSA-2048, SHA-256
+Transitional: RSA-4096, SHA-3
+Future:      Lattice-based, Hash-based Signatures
+```
+
+### Cloud-Native Evidence Management
+
+**Container-basierte Forensik-Pipelines**:
+```yaml
+# docker-compose.yml für Forensik-Lab
+version: '3.8'
+services:
+  evidence-intake:
+    image: forensics/evidence-intake:v2.1
+    volumes:
+      - ./evidence:/data
+    environment:
+      - AUTO_HASH=true
+      - BLOCKCHAIN_LOGGING=true
+  
+  chain-tracker:
+    image: forensics/chain-tracker:v1.5
+    depends_on:
+      - postgres
+    environment:
+      - DATABASE_URL=postgresql://user:pass@postgres:5432/custody
+```
+
+## Best Practices Zusammenfassung
+
+### Präventive Maßnahmen
+
+**1. Standardisierte Verfahren**
+```
+□ SOPs für alle Custody-Schritte
+□ Regelmäßige Team-Schulungen  
+□ Tool-Kalibrierung und -Wartung
+□ Backup-Verfahren für Ausfälle
+```
+
+**2. Technische Safeguards**
+```
+□ Redundante Hash-Algorithmen
+□ Automated Integrity Monitoring
+□ Secure Transport Protocols
+□ Environmental Monitoring
+```
+
+**3. Rechtliche Compliance**
+```
+□ Jurisdiction-spezifische SOPs
+□ Regular Legal Updates
+□ Attorney Consultation Process
+□ International Cooperation Agreements
+```
+
+### Reaktive Maßnahmen
+
+**Incident Response bei CoC-Verletzungen**:
+```
+1. Immediate Containment
+   - Stop all evidence processing
+   - Secure affected items
+   - Document incident details
+
+2. Impact Assessment  
+   - Determine scope of compromise
+   - Identify affected cases
+   - Assess legal implications
+
+3. Remediation
+   - Re-establish chain where possible
+   - Alternative evidence strategies
+   - Legal notification requirements
+
+4. Prevention
+   - Root cause analysis
+   - Process improvements
+   - Additional controls
+```
+
+## Fazit
+
+Die Chain of Custody ist mehr als eine administrative Pflicht - sie ist das **Fundament der digitalen Forensik**. Ohne ordnungsgemäße Beweiskette können selbst die stärksten technischen Beweise vor Gericht wertlos werden.
+
+**Schlüsselprinzipien für den Erfolg**:
+
+1. **Vorbereitung ist alles** - SOPs und Tools vor dem Incident
+2. **Dokumentation über alles** - Im Zweifel mehr dokumentieren
+3. **Technologie als Enabler** - Automatisierung wo möglich
+4. **Menschen im Fokus** - Training und Awareness entscheidend
+5. **Kontinuierliche Verbesserung** - Lessons Learned Integration
+
+Die Investition in robuste Chain of Custody Verfahren zahlt sich langfristig aus - durch höhere Erfolgsraten vor Gericht, reduzierte Compliance-Risiken und erhöhte Glaubwürdigkeit der forensischen Arbeit.
+
+> **Merksatz**: "Eine Kette ist nur so stark wie ihr schwächstes Glied - in der digitalen Forensik ist das oft die menschliche Komponente, nicht die technische."
+
+## Weiterführende Ressourcen
+
+**Standards und Guidelines**:
+- [ISO/IEC 27037:2012](https://www.iso.org/standard/44381.html) - Digital Evidence Guidelines
+- [NIST SP 800-86](https://csrc.nist.gov/publications/detail/sp/800-86/final) - Computer Forensics Guide
+- [RFC 3227](https://tools.ietf.org/html/rfc3227) - Evidence Collection Guidelines
+
+**Training und Zertifizierung**:
+- SANS FOR500 (Windows Forensic Analysis)
+- SANS FOR508 (Advanced Incident Response)
+- IACIS Certified Forensic Computer Examiner (CFCE)
+- CISSP (Chain of Custody Domain)
+
+**Tools und Software**:
+- [FTK Imager](https://www.exterro.com/digital-forensics-software/ftk-imager) - Free Imaging Tool
+- [Autopsy](https://www.sleuthkit.org/autopsy/) - Open Source Platform
+- [MSAB XRY](https://www.msab.com/) - Mobile Forensics
+- [Cellebrite UFED](https://www.cellebrite.com/) - Mobile Evidence Extraction
\ No newline at end of file
diff --git a/src/content/knowledgebase/concept-file-system-storage-forensics.md b/src/content/knowledgebase/concept-file-system-storage-forensics.md
new file mode 100644
index 0000000..998c16c
--- /dev/null
+++ b/src/content/knowledgebase/concept-file-system-storage-forensics.md
@@ -0,0 +1,471 @@
+---
+title: "Dateisystem-Forensik: Von NTFS-Strukturen bis Cloud-Storage-Artefakten"
+description: "Umfassender Leitfaden zur forensischen Analyse von Dateisystemen - NTFS-Metadaten, ext4-Journaling, APFS-Snapshots und Cloud-Storage-Forensik für professionelle Datenrekonstruktion"
+author: "Claude 4 Sonnett (Prompt: Mario Stöckl)"
+last_updated: 2025-08-10
+difficulty: intermediate
+categories: ["analysis", "configuration", "troubleshooting"]
+tags: ["filesystem-analysis", "metadata-extraction", "deleted-data-recovery", "slack-space", "journaling-analysis", "timestamp-forensics", "partition-analysis", "cloud-storage", "ntfs", "ext4", "apfs", "data-carving"]
+tool_name: "File Systems & Storage Forensics"
+related_tools: ["Autopsy", "The Sleuth Kit", "FTK Imager", "Volatility", "X-Ways Forensics"]
+published: true
+---
+
+# Dateisystem-Forensik: Von NTFS-Strukturen bis Cloud-Storage-Artefakten
+
+Die forensische Analyse von Dateisystemen bildet das Fundament moderner Digital Forensics. Dieser umfassende Leitfaden behandelt die kritischen Aspekte der Dateisystem-Forensik von traditionellen lokalen Speichermedien bis hin zu modernen Cloud-Storage-Umgebungen.
+
+## Grundlagen der Dateisystem-Forensik
+
+### Was ist Dateisystem-Forensik?
+
+Dateisystem-Forensik umfasst die systematische Untersuchung von Speicherstrukturen zur Rekonstruktion digitaler Beweise. Dabei werden nicht nur sichtbare Dateien analysiert, sondern auch Metadaten, gelöschte Inhalte und versteckte Artefakte untersucht.
+
+### Zentrale forensische Konzepte
+
+**Metadaten-Analyse**: Jedes Dateisystem speichert umfangreiche Metadaten über Dateien, Verzeichnisse und Systemaktivitäten. Diese Informationen sind oft aussagekräftiger als der eigentliche Dateiinhalt.
+
+**Slack Space**: Der ungenutzte Bereich zwischen dem Ende einer Datei und dem Ende des zugewiesenen Clusters kann Reste vorheriger Dateien enthalten.
+
+**Journaling**: Moderne Dateisysteme protokollieren Änderungen in Journal-Dateien, die wertvolle Timeline-Informationen liefern.
+
+**Timeline-Rekonstruktion**: Durch Kombination verschiedener Timestamp-Quellen lassen sich detaillierte Aktivitätszeitlinien erstellen.
+
+## NTFS-Forensik: Das Windows-Dateisystem im Detail
+
+### Master File Table (MFT) Analyse
+
+Die MFT ist das Herzstück von NTFS und enthält Einträge für jede Datei und jeden Ordner auf dem Volume.
+
+**Struktur eines MFT-Eintrags:**
+```
+Offset 0x00: FILE-Signatur
+Offset 0x04: Update Sequence Array Offset
+Offset 0x06: Update Sequence Array Größe
+Offset 0x08: $LogFile Sequence Number (LSN)
+Offset 0x10: Sequence Number
+Offset 0x12: Hard Link Count
+Offset 0x14: Erste Attribut-Offset
+```
+
+**Forensisch relevante Attribute:**
+- `$STANDARD_INFORMATION`: Timestamps, Dateiberechtigungen
+- `$FILE_NAME`: Dateiname, zusätzliche Timestamps
+- `$DATA`: Dateiinhalt oder Cluster-Referenzen
+- `$SECURITY_DESCRIPTOR`: Zugriffsberechtigungen
+
+**Praktische Analyse-Techniken:**
+
+1. **Gelöschte MFT-Einträge identifizieren**: Einträge mit FILE0-Signatur sind oft gelöschte Dateien
+2. **Timeline-Anomalien erkennen**: Vergleich zwischen $STANDARD_INFORMATION und $FILE_NAME Timestamps
+3. **Resident vs. Non-Resident Data**: Kleine Dateien (< 700 Bytes) werden direkt in der MFT gespeichert
+
+### $LogFile Analyse für Aktivitäts-Tracking
+
+Das NTFS-Journal protokolliert alle Dateisystem-Änderungen und ermöglicht detaillierte Aktivitäts-Rekonstruktion.
+
+**Relevante Log-Record-Typen:**
+- `CreateFile`: Datei-/Ordnererstellung
+- `DeleteFile`: Löschvorgänge
+- `RenameFile`: Umbenennungen
+- `SetInformationFile`: Metadaten-Änderungen
+
+**Analyse-Workflow:**
+```bash
+# Mit istat (Sleuth Kit) MFT-Eintrag analysieren
+istat /dev/sda1 5  # MFT-Eintrag 5 anzeigen
+
+# Mit fls gelöschte Dateien auflisten
+fls -r -d /dev/sda1
+
+# Mit tsk_recover gelöschte Dateien wiederherstellen
+tsk_recover /dev/sda1 /recovery/
+```
+
+### Alternate Data Streams (ADS) Detection
+
+ADS können zur Datenverbergung missbraucht werden und sind oft übersehen.
+
+**Erkennungsstrategien:**
+1. **MFT-Analyse auf mehrere $DATA-Attribute**: Dateien mit ADS haben multiple $DATA-Einträge
+2. **Powershell-Erkennung**: `Get-Item -Path C:\file.txt -Stream *`
+3. **Forensik-Tools**: Autopsy zeigt ADS automatisch in der File-Analyse
+
+### Volume Shadow Copies für Timeline-Rekonstruktion
+
+VSCs bieten Snapshots des Dateisystems zu verschiedenen Zeitpunkten.
+
+**Forensische Relevanz:**
+- Wiederherstellung gelöschter/überschriebener Dateien
+- Timeline-Rekonstruktion über längere Zeiträume
+- Registry-Hive-Vergleiche zwischen Snapshots
+
+**Zugriff auf VSCs:**
+```cmd
+# VSCs auflisten
+vssadmin list shadows
+
+# VSC mounten
+vshadow -p C: -script=shadow.cmd
+```
+
+## ext4-Forensik: Linux-Dateisystem-Analyse
+
+### Ext4-Journal-Analyse
+
+Das ext4-Journal (`/journal`) protokolliert Transaktionen und bietet wertvolle forensische Artefakte.
+
+**Journal-Struktur:**
+- **Descriptor Blocks**: Beschreiben bevorstehende Transaktionen
+- **Data Blocks**: Enthalten die eigentlichen Datenänderungen
+- **Commit Blocks**: Markieren abgeschlossene Transaktionen
+- **Revoke Blocks**: Listen widerrufene Blöcke auf
+
+**Praktische Analyse:**
+```bash
+# Journal-Informationen anzeigen
+tune2fs -l /dev/sda1 | grep -i journal
+
+# Mit debugfs Journal untersuchen
+debugfs /dev/sda1
+debugfs: logdump -a journal_file
+
+# Ext4-Metadaten extrahieren
+icat /dev/sda1 8 > journal.raw  # Inode 8 ist typisch das Journal
+```
+
+### Inode-Struktur und Deleted-File-Recovery
+
+**Ext4-Inode-Aufbau:**
+```
+struct ext4_inode {
+    __le16 i_mode;        # Dateityp und Berechtigungen
+    __le16 i_uid;         # Benutzer-ID
+    __le32 i_size;        # Dateigröße
+    __le32 i_atime;       # Letzter Zugriff
+    __le32 i_ctime;       # Inode-Änderung
+    __le32 i_mtime;       # Letzte Modifikation
+    __le32 i_dtime;       # Löschzeitpunkt
+    ...
+    __le32 i_block[EXT4_N_BLOCKS]; # Block-Pointer
+};
+```
+
+**Recovery-Techniken:**
+1. **Inode-Scanning**: Suche nach Inodes mit gesetztem dtime aber erhaltenen Blöcken
+2. **Journal-Recovery**: Replay von Journal-Einträgen vor Löschzeitpunkt
+3. **Directory-Entry-Recovery**: Undelfs-Techniken für kürzlich gelöschte Dateien
+
+### Extended Attributes (xattr) Forensik
+
+Extended Attributes speichern zusätzliche Metadaten und Sicherheitskontext.
+
+**Forensisch relevante xattrs:**
+- `security.selinux`: SELinux-Kontext
+- `user.*`: Benutzerdefinierte Attribute
+- `system.posix_acl_*`: ACL-Informationen
+- `security.capability`: File-Capabilities
+
+```bash
+# Alle xattrs einer Datei anzeigen
+getfattr -d /path/to/file
+
+# Spezifisches Attribut extrahieren
+getfattr -n user.comment /path/to/file
+```
+
+## APFS und HFS+ Forensik: macOS-Dateisysteme
+
+### APFS-Snapshots für Point-in-Time-Analysis
+
+APFS erstellt automatisch Snapshots, die forensische Goldgruben darstellen.
+
+**Snapshot-Management:**
+```bash
+# Snapshots auflisten
+tmutil listlocalsnapshots /
+
+# Snapshot mounten
+diskutil apfs mount -snapshot snapshot_name
+
+# Snapshot-Metadaten analysieren
+diskutil apfs list
+```
+
+**Forensische Anwendung:**
+- Vergleich von Dateisystem-Zuständen über Zeit
+- Recovery von gelöschten/modifizierten Dateien
+- Malware-Persistenz-Analyse
+
+### HFS+-Katalog-Datei-Forensik
+
+Die Katalog-Datei ist das Äquivalent zur NTFS-MFT in HFS+.
+
+**Struktur:**
+- **Header Node**: Baum-Metadaten
+- **Index Nodes**: Verweise auf Leaf Nodes
+- **Leaf Nodes**: Eigentliche Datei-/Ordner-Records
+- **Map Nodes**: Freie/belegte Nodes
+
+**Forensische Techniken:**
+```bash
+# Mit hfsdump Katalog analysieren
+hfsdump -c /dev/disk1s1
+
+# Gelöschte Dateien suchen
+fls -r -f hfsplus /dev/disk1s1
+```
+
+## Cloud Storage Forensics
+
+### OneDrive-Artefakt-Analyse
+
+**Lokale Artefakte:**
+- `%USERPROFILE%\OneDrive\*`: Synchronisierte Dateien
+- Registry: `HKCU\Software\Microsoft\OneDrive`
+- Event Logs: OneDrive-spezifische Ereignisse
+
+**Forensische Analyse-Punkte:**
+1. **Sync-Status**: Welche Dateien wurden synchronisiert?
+2. **Conflict-Resolution**: Wie wurden Konflikte gelöst?
+3. **Version-History**: Zugriff auf vorherige Datei-Versionen
+4. **Sharing-Activities**: Geteilte Dateien und Berechtigungen
+
+```powershell
+# OneDrive-Status abfragen
+Get-ItemProperty -Path "HKCU:\Software\Microsoft\OneDrive\Accounts\*"
+
+# Sync-Engine-Logs analysieren
+Get-WinEvent -LogName "Microsoft-Windows-OneDrive/Operational"
+```
+
+### Google Drive Forensik
+
+**Client-seitige Artefakte:**
+- `%LOCALAPPDATA%\Google\Drive\*`: Lokaler Cache
+- SQLite-Datenbanken: Sync-Metadaten
+- Temporary Files: Unvollständige Downloads
+
+**Wichtige Datenbanken:**
+- `sync_config.db`: Sync-Konfiguration
+- `cloud_graph.db`: Cloud-Dateienstruktur
+- `metadata_database`: Datei-Metadaten
+
+```bash
+# SQLite-Datenbank analysieren
+sqlite3 sync_config.db
+.tables
+SELECT * FROM data WHERE key LIKE '%sync%';
+```
+
+### Dropbox-Forensik
+
+**Forensische Artefakte:**
+- `%APPDATA%\Dropbox\*`: Konfiguration und Logs
+- `.dropbox.cache\*`: Lokaler Cache
+- Database-Dateien: Sync-Historie
+
+**Wichtige Dateien:**
+- `config.dbx`: Verschlüsselte Konfiguration
+- `filecache.dbx`: Datei-Cache-Informationen
+- `deleted.dbx`: Gelöschte Dateien-Tracking
+
+## File Carving und Datenrekonstruktion
+
+### Header/Footer-basiertes Carving
+
+**Klassische Ansätze:**
+```bash
+# Mit foremost File-Carving durchführen
+foremost -t jpg,pdf,doc -i /dev/sda1 -o /recovery/
+
+# Mit scalpel erweiterte Pattern verwenden
+scalpel -b -o /recovery/ /dev/sda1
+
+# Mit photorec interaktives Recovery
+photorec /dev/sda1
+```
+
+**Custom Carving-Patterns:**
+```
+# scalpel.conf Beispiel
+jpg	y	200000000	\xff\xd8\xff\xe0\x00\x10	\xff\xd9
+pdf	y	200000000	%PDF-	%%EOF\x0d
+zip	y	100000000	PK\x03\x04	PK\x05\x06
+```
+
+### Fragmentierte Datei-Rekonstruktion
+
+**Bifragment-Gap-Carving:**
+1. Identifikation von Header-Fragmenten
+2. Berechnung wahrscheinlicher Fragment-Größen
+3. Gap-Analyse zwischen Fragmenten
+4. Reassembly mit Plausibilitätsprüfung
+
+**Smart-Carving-Techniken:**
+- Semantic-aware Carving für Office-Dokumente
+- JPEG-Quantization-Table-Matching
+- Video-Keyframe-basierte Rekonstruktion
+
+## Timestamp-Manipulation und -Analyse
+
+### MACB-Timeline-Erstellung
+
+**Timestamp-Kategorien:**
+- **M** (Modified): Letzter Schreibzugriff auf Dateiinhalt
+- **A** (Accessed): Letzter Lesezugriff (oft deaktiviert)
+- **C** (Changed): Metadaten-Änderung (Inode/MFT)
+- **B** (Born): Erstellungszeitpunkt
+
+```bash
+# Mit fls Timeline erstellen
+fls -r -m C: > timeline.bodyfile
+mactime -d -b timeline.bodyfile > timeline.csv
+
+# Mit log2timeline umfassende Timeline
+log2timeline.py --storage-file timeline.plaso image.dd
+psort.py -o l2tcsv -w timeline_full.csv timeline.plaso
+```
+
+### Timestamp-Manipulation-Detection
+
+**Erkennungsstrategien:**
+1. **Chronologie-Anomalien**: Created > Modified Timestamps
+2. **Präzisions-Analyse**: Unnatürliche Rundung auf Sekunden/Minuten
+3. **Filesystem-Vergleich**: Inkonsistenzen zwischen verschiedenen Timestamp-Quellen
+4. **Batch-Manipulation**: Verdächtige Muster bei mehreren Dateien
+
+**Registry-basierte Evidenz:**
+```
+HKLM\SYSTEM\CurrentControlSet\Control\FileSystem\NtfsDisableLastAccessUpdate
+```
+
+## Häufige Herausforderungen und Lösungsansätze
+
+### Performance-Optimierung bei großen Images
+
+**Problem**: Analyse von Multi-TB-Images dauert Tage
+**Lösungen**:
+1. **Selective Processing**: Nur relevante Partitionen analysieren
+2. **Parallel Processing**: Multi-threaded Tools verwenden
+3. **Hardware-Optimierung**: NVMe-SSDs für temporäre Dateien
+4. **Cloud-Processing**: Verteilte Analyse in der Cloud
+
+### Verschlüsselte Container und Volumes
+
+**BitLocker-Forensik**:
+```bash
+# Mit dislocker BitLocker-Volume mounten
+dislocker -r -V /dev/sda1 -p password -- /tmp/bitlocker
+
+# Recovery-Key-basierter Zugriff
+dislocker -r -V /dev/sda1 -k recovery.key -- /tmp/bitlocker
+```
+
+**VeraCrypt-Analyse**:
+- Header-Backup-Analyse für mögliche Passwort-Recovery
+- Hidden-Volume-Detection durch Entropie-Analyse
+- Keyfile-basierte Entschlüsselung
+
+### Anti-Forensik-Techniken erkennen
+
+**Wiping-Detection**:
+- Pattern-Analyse für DoD 5220.22-M Wiping
+- Random-Data vs. Encrypted-Data Unterscheidung
+- Unvollständige Wiping-Artefakte
+
+**Timestomp-Detection**:
+```bash
+# Mit analyzeMFT.py Timestamp-Anomalien finden
+analyzeMFT.py -f $MFT -o analysis.csv
+# Analyse der $SI vs. $FN Timestamp-Diskrepanzen
+```
+
+## Tool-Integration und Workflows
+
+### Autopsy-Integration
+
+**Workflow-Setup**:
+1. **Image-Import**: E01/DD-Images mit Hash-Verifikation
+2. **Ingest-Module**: File-Type-Detection, Hash-Lookup, Timeline-Creation
+3. **Analysis**: Keyword-Search, Timeline-Analysis, File-Category-Review
+4. **Reporting**: Automatisierte Report-Generierung
+
+### TSK-Kommandozeilen-Pipeline
+
+```bash
+#!/bin/bash
+# Vollständiger Dateisystem-Analyse-Workflow
+
+IMAGE="/cases/evidence.dd"
+OUTPUT="/analysis/case001"
+
+# 1. Partitionstabelle analysieren
+mmls "$IMAGE" > "$OUTPUT/partitions.txt"
+
+# 2. Dateisystem-Info extrahieren
+fsstat "$IMAGE" > "$OUTPUT/filesystem_info.txt"
+
+# 3. Timeline erstellen
+fls -r -m "$IMAGE" > "$OUTPUT/timeline.bodyfile"
+mactime -d -b "$OUTPUT/timeline.bodyfile" > "$OUTPUT/timeline.csv"
+
+# 4. Gelöschte Dateien auflisten
+fls -r -d "$IMAGE" > "$OUTPUT/deleted_files.txt"
+
+# 5. File-Carving durchführen
+foremost -t all -i "$IMAGE" -o "$OUTPUT/carved/"
+
+# 6. Hash-Analyse
+hfind -i nsrl "$OUTPUT/timeline.bodyfile" > "$OUTPUT/known_files.txt"
+```
+
+## Best Practices und Methodologie
+
+### Dokumentation und Chain of Custody
+
+**Kritische Dokumentationspunkte**:
+1. **Acquisition-Details**: Tool, Version, Hash-Werte, Zeitstempel
+2. **Analysis-Methodik**: Verwendete Tools und Parameter
+3. **Findings-Dokumentation**: Screenshots, Befund-Zusammenfassung
+4. **Timeline-Rekonstruktion**: Chronologische Ereignis-Dokumentation
+
+### Qualitätssicherung
+
+**Verifikations-Checkliste**:
+- [ ] Hash-Integrität von Original-Images
+- [ ] Tool-Version-Dokumentation
+- [ ] Kreuz-Validierung mit verschiedenen Tools
+- [ ] Timeline-Plausibilitätsprüfung
+- [ ] Anti-Forensik-Artefakt-Suche
+
+### Rechtliche Aspekte
+
+**Admissibility-Faktoren**:
+1. **Tool-Reliability**: Verwendung etablierter, validierter Tools
+2. **Methodology-Documentation**: Nachvollziehbare Analyse-Schritte
+3. **Error-Rate-Analysis**: Bekannte Limitationen dokumentieren
+4. **Expert-Qualification**: Forensiker-Qualifikation nachweisen
+
+## Weiterführende Ressourcen
+
+### Spezialisierte Tools
+- **X-Ways Forensics**: Kommerzielle All-in-One-Lösung
+- **EnCase**: Enterprise-Forensik-Platform
+- **AXIOM**: Mobile und Computer-Forensik
+- **Oxygen Detective**: Mobile-Spezialist
+- **BlackBag**: macOS-Forensik-Spezialist
+
+### Fortgeschrittene Techniken
+- **Memory-Forensics**: Volatility für RAM-Analyse
+- **Network-Forensics**: Wireshark für Netzwerk-Traffic
+- **Mobile-Forensics**: Cellebrite/Oxygen für Smartphone-Analyse
+- **Cloud-Forensics**: KAPE für Cloud-Artefakt-Collection
+
+### Continuous Learning
+- **SANS FOR508**: Advanced Digital Forensics
+- **Volatility Training**: Memory-Forensics-Spezialisierung
+- **FIRST Conference**: Internationale Forensik-Community
+- **DFRWS**: Digital Forensics Research Workshop
+
+Die moderne Dateisystem-Forensik erfordert ein tiefes Verständnis verschiedener Speichertechnologien und deren forensischer Artefakte. Durch systematische Anwendung der beschriebenen Techniken und kontinuierliche Weiterbildung können Forensiker auch komplexeste Fälle erfolgreich bearbeiten und gerichtsfeste Beweise sicherstellen.
\ No newline at end of file
diff --git a/src/content/knowledgebase/concept-hash-functions.md b/src/content/knowledgebase/concept-hash-functions.md
new file mode 100644
index 0000000..02204f6
--- /dev/null
+++ b/src/content/knowledgebase/concept-hash-functions.md
@@ -0,0 +1,377 @@
+---
+title: "Hash-Funktionen und digitale Signaturen: Grundlagen der digitalen Beweissicherung"
+description: "Umfassender Leitfaden zu kryptographischen Hash-Funktionen, digitalen Signaturen und deren praktischer Anwendung in der digitalen Forensik für Integritätsprüfung und Beweissicherung"
+author: "Claude 4 Sonnett (Prompt: Mario Stöckl)"
+last_updated: 2025-08-10
+difficulty: advanced
+categories: ["analysis", "configuration", "case-study"]
+tags: ["hashing", "integrity-check", "chain-of-custody", "standards-compliant", "deduplication", "known-bad-detection", "fuzzy-hashing", "digital-signatures", "timestamping", "blockchain-evidence", "md5", "sha256", "ssdeep"]
+tool_name: "Hash Functions & Digital Signatures"
+published: true
+---
+
+# Hash-Funktionen und digitale Signaturen: Grundlagen der digitalen Beweissicherung
+
+Hash-Funktionen und digitale Signaturen bilden das fundamentale Rückgrat der digitalen Forensik. Sie gewährleisten die Integrität von Beweismitteln, ermöglichen die Authentifizierung von Daten und sind essentiell für die rechtssichere Dokumentation forensischer Untersuchungen.
+
+## Was sind kryptographische Hash-Funktionen?
+
+Eine kryptographische Hash-Funktion ist ein mathematisches Verfahren, das aus beliebig großen Eingabedaten einen festen, eindeutigen "Fingerabdruck" (Hash-Wert) erzeugt. Dieser Wert verändert sich drastisch, wenn auch nur ein einzelnes Bit der Eingabe modifiziert wird.
+
+### Eigenschaften einer kryptographischen Hash-Funktion
+
+**Einwegfunktion (One-Way Function)**
+- Aus dem Hash-Wert kann nicht auf die ursprünglichen Daten geschlossen werden
+- Mathematisch praktisch irreversibel
+
+**Determinismus**
+- Identische Eingabe erzeugt immer identischen Hash-Wert
+- Reproduzierbare Ergebnisse für forensische Dokumentation
+
+**Kollisionsresistenz**
+- Extrem schwierig, zwei verschiedene Eingaben zu finden, die denselben Hash erzeugen
+- Gewährleistet Eindeutigkeit in forensischen Anwendungen
+
+**Lawineneffekt**
+- Minimale Änderung der Eingabe führt zu völlig anderem Hash-Wert
+- Erkennung von Manipulationen
+
+## Wichtige Hash-Algorithmen in der Forensik
+
+### MD5 (Message Digest Algorithm 5)
+```bash
+# MD5-Hash berechnen
+md5sum evidence.dd
+# Output: 5d41402abc4b2a76b9719d911017c592  evidence.dd
+```
+
+**Eigenschaften:**
+- 128-Bit Hash-Wert (32 Hexadezimal-Zeichen)
+- Entwickelt 1991, kryptographisch gebrochen seit 2004
+- **Nicht mehr sicher**, aber weit verbreitet in Legacy-Systemen
+- Kollisionen sind praktisch erzeugbar
+
+**Forensische Relevanz:**
+- Noch in vielen bestehenden Systemen verwendet
+- Für forensische Zwecke nur bei bereits vorhandenen MD5-Hashes
+- Niemals für neue forensische Implementierungen verwenden
+
+### SHA-1 (Secure Hash Algorithm 1)
+```bash
+# SHA-1-Hash berechnen
+sha1sum evidence.dd
+# Output: aaf4c61ddcc5e8a2dabede0f3b482cd9aea9434d  evidence.dd
+```
+
+**Eigenschaften:**
+- 160-Bit Hash-Wert (40 Hexadezimal-Zeichen)
+- Entwickelt von NSA, standardisiert 1995
+- **Deprecated seit 2017** aufgrund praktischer Kollisionsangriffe
+- SHAttered-Angriff bewies Schwachstellen 2017
+
+### SHA-2-Familie (SHA-256, SHA-512)
+```bash
+# SHA-256-Hash berechnen
+sha256sum evidence.dd
+# Output: e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855  evidence.dd
+
+# SHA-512-Hash berechnen
+sha512sum evidence.dd
+```
+
+**SHA-256 Eigenschaften:**
+- 256-Bit Hash-Wert (64 Hexadezimal-Zeichen)
+- Aktueller Standard für forensische Anwendungen
+- NIST-approved, FIPS 180-4 konform
+- Keine bekannten praktischen Angriffe
+
+**SHA-512 Eigenschaften:**
+- 512-Bit Hash-Wert (128 Hexadezimal-Zeichen)
+- Höhere Sicherheit, aber größerer Hash-Wert
+- Optimal für hochsensible Ermittlungen
+
+### SHA-3 (Keccak)
+- Neuester Standard (seit 2015)
+- Andere mathematische Grundlage als SHA-2
+- Zukünftiger Standard bei SHA-2-Kompromittierung
+
+## Forensische Anwendungen von Hash-Funktionen
+
+### 1. Datenträger-Imaging und Verifikation
+
+**Vor dem Imaging:**
+```bash
+# Original-Datenträger hashen
+sha256sum /dev/sdb > original_hash.txt
+```
+
+**Nach dem Imaging:**
+```bash
+# Image-Datei hashen
+sha256sum evidence.dd > image_hash.txt
+
+# Vergleichen
+diff original_hash.txt image_hash.txt
+```
+
+**Best Practice:**
+- Immer mehrere Hash-Algorithmen verwenden (SHA-256 + SHA-512)
+- Hash-Berechnung vor, während und nach dem Imaging
+- Dokumentation in Chain-of-Custody-Protokoll
+
+### 2. Deduplizierung mit Hash-Sets
+
+Hash-Sets ermöglichen die Identifikation bekannter Dateien zur Effizienzsteigerung:
+
+**NSRL (National Software Reference Library)**
+```bash
+# NSRL-Hash-Set laden
+autopsy --load-hashset /path/to/nsrl/NSRLFile.txt
+
+# Bekannte Dateien ausschließen
+hashdeep -s -e nsrl_hashes.txt /evidence/mount/
+```
+
+**Eigene Hash-Sets erstellen:**
+```bash
+# Hash-Set von bekannten guten Dateien
+hashdeep -r /clean_system/ > clean_system_hashes.txt
+
+# Vergleich mit verdächtigem System
+hashdeep -s -e clean_system_hashes.txt /suspect_system/
+```
+
+### 3. Known-Bad-Erkennung
+
+**Malware-Hash-Datenbanken:**
+- VirusTotal API-Integration
+- Threat Intelligence Feeds
+- Custom IoC-Listen
+
+```python
+# Beispiel: Datei-Hash gegen Known-Bad-Liste prüfen
+import hashlib
+
+def check_malware_hash(filepath, malware_hashes):
+    with open(filepath, 'rb') as f:
+        file_hash = hashlib.sha256(f.read()).hexdigest()
+    
+    if file_hash in malware_hashes:
+        return True, file_hash
+    return False, file_hash
+```
+
+### 4. Fuzzy Hashing mit ssdeep
+
+Fuzzy Hashing erkennt ähnliche, aber nicht identische Dateien:
+
+```bash
+# ssdeep-Hash berechnen
+ssdeep malware.exe
+# Output: 768:gQA1M2Ua3QqQm8+1QV7Q8+1QG8+1Q:gQ1Ma3qmP1QV7P1QGP1Q
+
+# Ähnlichkeit zwischen Dateien prüfen
+ssdeep -d malware_v1.exe malware_v2.exe
+# Output: 85 (85% Ähnlichkeit)
+```
+
+**Anwendungsfälle:**
+- Erkennung von Malware-Varianten
+- Identifikation modifizierter Dokumente
+- Versionsverfolgung von Dateien
+
+### 5. Timeline-Analyse und Integritätsprüfung
+
+```bash
+# Erweiterte Metadaten mit Hashes
+find /evidence/mount -type f -exec stat -c "%Y %n" {} \; | while read timestamp file; do
+    hash=$(sha256sum "$file" | cut -d' ' -f1)
+    echo "$timestamp $hash $file"
+done > timeline_with_hashes.txt
+```
+
+## Digitale Signaturen in der Forensik
+
+Digitale Signaturen verwenden asymmetrische Kryptographie zur Authentifizierung und Integritätssicherung.
+
+### Funktionsweise digitaler Signaturen
+
+1. **Erstellung:**
+   - Hash des Dokuments wird mit privatem Schlüssel verschlüsselt
+   - Verschlüsselter Hash = digitale Signatur
+
+2. **Verifikation:**
+   - Signatur wird mit öffentlichem Schlüssel entschlüsselt
+   - Entschlüsselter Hash wird mit neuem Hash des Dokuments verglichen
+
+### Certificate Chain Analysis
+
+**X.509-Zertifikate untersuchen:**
+```bash
+# Zertifikat-Details anzeigen
+openssl x509 -in certificate.crt -text -noout
+
+# Zertifikatskette verfolgen
+openssl verify -CAfile ca-bundle.crt -untrusted intermediate.crt certificate.crt
+```
+
+**Forensische Relevanz:**
+- Authentizität von Software-Downloads
+- Erkennung gefälschter Zertifikate
+- APT-Gruppenattribution durch Code-Signing-Zertifikate
+
+### Timestamping für Chain-of-Custody
+
+**RFC 3161-Zeitstempel:**
+```bash
+# Zeitstempel für Beweisdatei erstellen
+openssl ts -query -data evidence.dd -no_nonce -sha256 -out request.tsq
+openssl ts -verify -in response.tsr -data evidence.dd -CAfile tsa-ca.crt
+```
+
+**Blockchain-basierte Zeitstempel:**
+- Unveränderliche Zeitstempel in öffentlichen Blockchains
+- OriginStamp, OpenTimestamps für forensische Anwendungen
+
+## Praktische Tools und Integration
+
+### Autopsy Integration
+```xml
+<!-- Autopsy Hash Database Configuration -->
+<hashDb>
+    <dbType>NSRL</dbType>
+    <dbPath>/usr/share/autopsy/nsrl/NSRLFile.txt</dbPath>
+    <searchDuringIngest>true</searchDuringIngest>
+</hashDb>
+```
+
+### YARA-Integration mit Hash-Regeln
+```yara
+rule Malware_Hash_Detection {
+    condition:
+        hash.sha256(0, filesize) == "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855"
+}
+```
+
+### FTK Imager Hash-Verifikation
+- Automatische Hash-Berechnung während Imaging
+- MD5, SHA-1, SHA-256 parallel
+- Verify-Funktion für Image-Integrität
+
+## Advanced Topics
+
+### Rainbow Table Attacks
+**Funktionsweise:**
+- Vorberechnete Hash-Tabellen für Passwort-Cracking
+- Trade-off zwischen Speicher und Rechenzeit
+- Effektiv gegen unsalted Hashes
+
+**Forensische Anwendung:**
+```bash
+# Hashcat mit Rainbow Tables
+hashcat -m 0 -a 0 hashes.txt wordlist.txt
+
+# John the Ripper mit Rainbow Tables
+john --format=NT --wordlist=rockyou.txt ntlm_hashes.txt
+```
+
+### Blockchain Evidence Management
+**Konzept:**
+- Unveränderliche Speicherung von Hash-Werten
+- Distributed Ledger für Chain-of-Custody
+- Smart Contracts für automatisierte Verifikation
+
+**Implementierung:**
+```solidity
+// Ethereum Smart Contract für Evidence Hashes
+contract EvidenceRegistry {
+    mapping(bytes32 => bool) public evidenceHashes;
+    
+    function registerEvidence(bytes32 _hash) public {
+        evidenceHashes[_hash] = true;
+    }
+}
+```
+
+## Häufige Probleme und Lösungsansätze
+
+### Hash-Kollisionen
+**Problem:** Zwei verschiedene Dateien mit identischem Hash
+**Lösung:**
+- Verwendung mehrerer Hash-Algorithmen
+- Sichere Algorithmen (SHA-256+) verwenden
+- Bei Verdacht: Bitweise Vergleich der Originaldateien
+
+### Performance bei großen Datenmengen
+**Problem:** Langsame Hash-Berechnung bei TB-großen Images
+**Optimierung:**
+```bash
+# Parallele Hash-Berechnung
+hashdeep -r -j 8 /large_dataset/  # 8 Threads
+
+# Hardware-beschleunigte Hashing
+sha256sum --tag /dev/nvme0n1  # NVMe für bessere I/O
+```
+
+### Rechtliche Anforderungen
+**Problem:** Verschiedene Standards in verschiedenen Jurisdiktionen
+**Lösung:**
+- NIST-konforme Algorithmen verwenden
+- Dokumentation aller verwendeten Verfahren
+- Regelmäßige Algorithmus-Updates
+
+## Best Practices
+
+### 1. Algorithmus-Auswahl
+- **Neu:** SHA-256 oder SHA-3 verwenden
+- **Legacy:** MD5/SHA-1 nur bei vorhandenen Systemen
+- **High-Security:** SHA-512 oder SHA-3-512
+
+### 2. Dokumentation
+```text
+Evidence Hash Verification Report
+=================================
+Evidence ID: CASE-2024-001-HDD
+Original Hash (SHA-256): a1b2c3d4...
+Image Hash (SHA-256): a1b2c3d4...
+Verification Status: VERIFIED
+Timestamp: 2024-01-15 14:30:00 UTC
+Investigator: John Doe
+```
+
+### 3. Redundanz
+- Mindestens zwei verschiedene Hash-Algorithmen
+- Mehrfache Verifikation zu verschiedenen Zeitpunkten
+- Verschiedene Tools für Cross-Validation
+
+### 4. Automation
+```bash
+#!/bin/bash
+# Automatisiertes Hash-Verification-Script
+EVIDENCE_FILE="$1"
+LOG_FILE="hash_verification.log"
+
+echo "Starting hash verification for $EVIDENCE_FILE" >> $LOG_FILE
+MD5_HASH=$(md5sum "$EVIDENCE_FILE" | cut -d' ' -f1)
+SHA256_HASH=$(sha256sum "$EVIDENCE_FILE" | cut -d' ' -f1)
+SHA512_HASH=$(sha512sum "$EVIDENCE_FILE" | cut -d' ' -f1)
+
+echo "MD5: $MD5_HASH" >> $LOG_FILE
+echo "SHA-256: $SHA256_HASH" >> $LOG_FILE
+echo "SHA-512: $SHA512_HASH" >> $LOG_FILE
+echo "Verification completed at $(date)" >> $LOG_FILE
+```
+
+## Zukunftsperspektiven
+
+### Quantum-Resistant Hashing
+- Vorbereitung auf Quantum Computing
+- NIST Post-Quantum Cryptography Standards
+- Migration bestehender Systeme
+
+### AI/ML-Integration
+- Anomalie-Erkennung in Hash-Mustern
+- Automated Similarity Analysis
+- Intelligent Deduplizierung
+
+Hash-Funktionen und digitale Signaturen sind und bleiben das Fundament der digitalen Forensik. Das Verständnis ihrer mathematischen Grundlagen, praktischen Anwendungen und rechtlichen Implikationen unterscheidet professionelle Forensiker von Amateuren. Mit der kontinuierlichen Weiterentwicklung der Technologie müssen auch forensische Praktiken angepasst werden, um die Integrität und Authentizität digitaler Beweise zu gewährleisten.
\ No newline at end of file
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+---
+title: "Memory Forensics und Process Analysis: Advanced Malware Detection in Volatile Memory"
+description: "Umfassender Leitfaden zur forensischen Analyse von Arbeitsspeicher-Strukturen, Process-Injection-Techniken und Advanced-Malware-Detection. Von Kernel-Analysis bis Cross-Platform-Memory-Forensik."
+author: "Claude 4 Sonnett (Prompt: Mario Stöckl)"
+last_updated: 2025-08-10
+difficulty: advanced
+categories: ["analysis", "advanced-techniques", "malware-investigation"]
+tags: ["memory-structures", "process-injection", "rootkit-detection", "kernel-analysis", "address-space", "live-analysis", "malware-hiding", "system-internals", "volatility", "dll-hollowing", "process-ghosting"]
+related_tools: ["Volatility 3", "Rekall", "WinDbg", "GDB"]
+published: true
+---
+
+# Memory Forensics und Process Analysis: Advanced Malware Detection in Volatile Memory
+
+Memory Forensics stellt eine der komplexesten und gleichzeitig aufschlussreichsten Disziplinen der digitalen Forensik dar. Während traditionelle Festplatten-Forensik auf persistente Daten zugreift, ermöglicht die Analyse des Arbeitsspeichers Einblicke in aktive Prozesse, verschleierte Malware und Angriffstechniken, die keine Spuren auf der Festplatte hinterlassen.
+
+## Einführung in Memory Forensics
+
+### Was ist Memory Forensics?
+
+Memory Forensics ist die Wissenschaft der Analyse von Computer-Arbeitsspeicher (RAM) zur Aufdeckung digitaler Artefakte. Im Gegensatz zur traditionellen Festplatten-Forensik konzentriert sich Memory Forensics auf volatile Daten, die nur temporär im Speicher existieren.
+
+**Zentrale Vorteile:**
+- Erkennung von Malware, die nur im Speicher residiert
+- Aufdeckung von Process-Injection und Code-Hiding-Techniken
+- Analyse von verschlüsselten oder obfuscierten Prozessen
+- Rekonstruktion von Netzwerkverbindungen und Benutzeraktivitäten
+- Untersuchung von Kernel-Level-Rootkits
+
+### Virtual Memory Layout verstehen
+
+Das Virtual Memory System moderner Betriebssysteme bildet die Grundlage für Memory Forensics. Jeder Prozess erhält einen eigenen virtuellen Adressraum, der in verschiedene Segmente unterteilt ist:
+
+**Windows Virtual Memory Layout:**
+```
+0x00000000 - 0x7FFFFFFF: User Space (2GB)
+0x80000000 - 0xFFFFFFFF: Kernel Space (2GB)
+
+User Space Segmente:
+- 0x00000000 - 0x0000FFFF: NULL Pointer Region
+- 0x00010000 - 0x7FFEFFFF: User Code und Data
+- 0x7FFF0000 - 0x7FFFFFFF: System DLLs (ntdll.dll)
+```
+
+**Linux Virtual Memory Layout:**
+```
+0x00000000 - 0xBFFFFFFF: User Space (3GB)
+0xC0000000 - 0xFFFFFFFF: Kernel Space (1GB)
+
+User Space Segmente:
+- Text Segment: Executable Code
+- Data Segment: Initialized Variables
+- BSS Segment: Uninitialized Variables
+- Heap: Dynamic Memory Allocation
+- Stack: Function Calls und Local Variables
+```
+
+## Process Internals und Strukturen
+
+### Process Control Blocks (PCB)
+
+Jeder Prozess wird durch eine zentrale Datenstruktur repräsentiert, die alle relevanten Informationen enthält:
+
+**Windows EPROCESS Structure:**
+```c
+typedef struct _EPROCESS {
+    KPROCESS Pcb;                    // Process Control Block
+    EX_PUSH_LOCK ProcessLock;        // Process Lock
+    LARGE_INTEGER CreateTime;        // Creation Timestamp
+    LARGE_INTEGER ExitTime;          // Exit Timestamp
+    EX_RUNDOWN_REF RundownProtect;   // Rundown Protection
+    HANDLE UniqueProcessId;          // Process ID (PID)
+    LIST_ENTRY ActiveProcessLinks;   // Double Linked List
+    RTL_AVL_TREE VadRoot;           // Virtual Address Descriptors
+    // ... weitere Felder
+} EPROCESS, *PEPROCESS;
+```
+
+**Wichtige Felder für Forensik:**
+- `ImageFileName`: Name der ausführbaren Datei
+- `Peb`: Process Environment Block Pointer
+- `VadRoot`: Virtual Address Descriptor Tree
+- `Token`: Security Token des Prozesses
+- `HandleTable`: Tabelle geöffneter Handles
+
+### Thread Control Blocks (TCB)
+
+Threads sind die ausführbaren Einheiten innerhalb eines Prozesses:
+
+**Windows ETHREAD Structure:**
+```c
+typedef struct _ETHREAD {
+    KTHREAD Tcb;                     // Thread Control Block
+    LARGE_INTEGER CreateTime;        // Thread Creation Time
+    LIST_ENTRY ThreadListEntry;      // Process Thread List
+    EX_RUNDOWN_REF RundownProtect;   // Rundown Protection
+    PEPROCESS ThreadsProcess;        // Parent Process Pointer
+    PVOID StartAddress;              // Thread Start Address
+    // ... weitere Felder
+} ETHREAD, *PETHREAD;
+```
+
+## Advanced Malware Detection Techniken
+
+### Process Injection Erkennung
+
+Process Injection ist eine häufig verwendete Technik zur Umgehung von Security-Lösungen. Verschiedene Injection-Methoden erfordern spezifische Erkennungsansätze:
+
+#### DLL Injection Detection
+
+**Erkennungsmerkmale:**
+```bash
+# Volatility 3 Command
+python vol.py -f memory.dmp windows.dlllist.DllList --pid 1234
+
+# Verdächtige Indikatoren:
+# - Ungewöhnliche DLL-Pfade
+# - DLLs ohne digitale Signatur
+# - Temporäre oder versteckte Pfade
+# - Diskrepanzen zwischen Image und Memory
+```
+
+**Manuelle Verifikation:**
+```python
+# Pseudocode für DLL-Validierung
+def validate_dll_integrity(dll_base, dll_path):
+    memory_hash = calculate_memory_hash(dll_base)
+    disk_hash = calculate_file_hash(dll_path)
+    
+    if memory_hash != disk_hash:
+        return "POTENTIAL_INJECTION_DETECTED"
+    return "CLEAN"
+```
+
+#### Process Hollowing Detection
+
+Process Hollowing ersetzt den ursprünglichen Code eines legitimen Prozesses:
+
+**Erkennungsmerkmale:**
+- Diskrepanz zwischen ImageFileName und tatsächlichem Code
+- Ungewöhnliche Memory Protection Flags
+- Fehlende oder modifizierte PE Header
+- Unerwartete Entry Points
+
+**Volatility Detection:**
+```bash
+# Process Hollowing Indicators
+python vol.py -f memory.dmp windows.malfind.Malfind
+python vol.py -f memory.dmp windows.vadinfo.VadInfo --pid 1234
+```
+
+#### Process Ghosting Detection
+
+Eine der neuesten Evasion-Techniken, die Prozesse ohne korrespondierende Dateien auf der Festplatte erstellt:
+
+**Erkennungsmerkmale:**
+```bash
+# File Object Analysis
+python vol.py -f memory.dmp windows.handles.Handles --pid 1234
+
+# Suche nach:
+# - Deleted File Objects
+# - Processes ohne korrespondierende Image Files
+# - Ungewöhnliche Creation Patterns
+```
+
+### DLL Hollowing und Memory Manipulation
+
+DLL Hollowing überschreibt legitimierte DLL-Sektionen mit malicious Code:
+
+**Detection Workflow:**
+1. **Section Analysis:**
+   ```bash
+   python vol.py -f memory.dmp windows.vadinfo.VadInfo --pid 1234
+   ```
+
+2. **Memory Permission Analysis:**
+   ```bash
+   # Suche nach ungewöhnlichen Permissions
+   # RWX (Read-Write-Execute) Bereiche sind verdächtig
+   ```
+
+3. **Entropy Analysis:**
+   ```python
+   def calculate_section_entropy(memory_region):
+       entropy = 0
+       for byte_value in range(256):
+           probability = memory_region.count(byte_value) / len(memory_region)
+           if probability > 0:
+               entropy += probability * math.log2(probability)
+       return -entropy
+   ```
+
+## Kernel-Level Analysis
+
+### System Call Hooking Detection
+
+Rootkits manipulieren häufig System Call Tables (SSDT):
+
+**Windows SSDT Analysis:**
+```bash
+# System Service Descriptor Table
+python vol.py -f memory.dmp windows.ssdt.SSDT
+
+# Verdächtige Indikatoren:
+# - Hooks außerhalb bekannter Module
+# - Ungewöhnliche Sprungadressen
+# - Modifizierte System Call Nummern
+```
+
+**Linux System Call Table:**
+```bash
+# System Call Table Analysis für Linux
+python vol.py -f linux.dmp linux.check_syscall.Check_syscall
+```
+
+### Driver Analysis
+
+Kernel-Mode-Rootkits nutzen Device Driver für persistente Angriffe:
+
+**Windows Driver Enumeration:**
+```bash
+# Loaded Modules Analysis
+python vol.py -f memory.dmp windows.modules.Modules
+
+# Driver IRP Analysis
+python vol.py -f memory.dmp windows.driverscan.DriverScan
+```
+
+**Verdächtige Driver-Eigenschaften:**
+- Fehlende Code-Signierung
+- Ungewöhnliche Load-Adressen
+- Versteckte oder gelöschte Driver-Files
+- Modifizierte IRP (I/O Request Packet) Handler
+
+### Rootkit Detection Methoden
+
+#### Direct Kernel Object Manipulation (DKOM)
+
+DKOM-Rootkits manipulieren Kernel-Datenstrukturen direkt:
+
+**Process Hiding Detection:**
+```bash
+# Process Scan vs. Process List Comparison
+python vol.py -f memory.dmp windows.psscan.PsScan > psscan.txt
+python vol.py -f memory.dmp windows.pslist.PsList > pslist.txt
+
+# Vergleich zeigt versteckte Prozesse
+diff psscan.txt pslist.txt
+```
+
+#### EPROCESS Link Manipulation
+
+```python
+# Pseudocode für EPROCESS Validation
+def validate_process_links(eprocess_list):
+    for process in eprocess_list:
+        flink = process.ActiveProcessLinks.Flink
+        blink = process.ActiveProcessLinks.Blink
+        
+        # Validate bidirectional links
+        if flink.Blink != process or blink.Flink != process:
+            return "LINK_MANIPULATION_DETECTED"
+```
+
+## Memory Dump Acquisition Strategien
+
+### Live Memory Acquisition
+
+**Windows Memory Acquisition:**
+```bash
+# DumpIt (Comae)
+DumpIt.exe /output C:\memory.dmp
+
+# WinPmem
+winpmem-2.1.post4.exe C:\memory.raw
+
+# Magnet RAM Capture
+MRCv1.20.exe /go /output C:\memory.dmp
+```
+
+**Linux Memory Acquisition:**
+```bash
+# LiME (Linux Memory Extractor)
+insmod lime.ko "path=/tmp/memory.lime format=lime"
+
+# AVML (Azure Virtual Machine Memory Extractor)
+./avml memory.dmp
+
+# dd (für /dev/mem falls verfügbar)
+dd if=/dev/mem of=memory.dd bs=1M
+```
+
+### Memory Acquisition Challenges
+
+**Volatility Considerations:**
+- Memory-Inhalte ändern sich kontinuierlich
+- Acquisition-Tools können Memory-Layout beeinflussen
+- Anti-Forensic-Techniken können Acquisition verhindern
+- Verschlüsselte Memory-Bereiche
+
+**Best Practices:**
+- Multiple Acquisition-Methoden verwenden
+- Acquisition-Logs dokumentieren
+- Hash-Werte für Integrität generieren
+- Timestamp-Synchronisation
+
+## Address Space Reconstruction
+
+### Virtual Address Translation
+
+Das Verständnis der Address Translation ist essentiell für Memory Forensics:
+
+**Windows Page Table Walkthrough:**
+```
+Virtual Address (32-bit): 
+┌─────────────┬─────────────┬──────────────┐
+│ PDE (10bit) │ PTE (10bit) │ Offset(12bit)│
+└─────────────┴─────────────┴──────────────┘
+
+1. Page Directory Entry → Page Table Base
+2. Page Table Entry → Physical Page Frame
+3. Offset → Byte within Physical Page
+```
+
+**Linux Page Table Structure:**
+```
+Virtual Address (64-bit):
+┌───┬───┬───┬───┬──────────┐
+│PGD│PUD│PMD│PTE│  Offset  │
+└───┴───┴───┴───┴──────────┘
+
+4-Level Page Table (x86_64):
+- PGD: Page Global Directory
+- PUD: Page Upper Directory  
+- PMD: Page Middle Directory
+- PTE: Page Table Entry
+```
+
+### Memory Mapping Analysis
+
+**Windows VAD (Virtual Address Descriptor) Trees:**
+```bash
+# VAD Tree Analysis
+python vol.py -f memory.dmp windows.vadinfo.VadInfo --pid 1234
+
+# Memory Mapping Details
+python vol.py -f memory.dmp windows.memmap.Memmap --pid 1234
+```
+
+**Linux Memory Maps:**
+```bash
+# Process Memory Maps
+python vol.py -f linux.dmp linux.proc_maps.Maps --pid 1234
+```
+
+## Cross-Platform Memory Forensics
+
+### Windows-Specific Artefakte
+
+**Registry in Memory:**
+```bash
+# Registry Hives
+python vol.py -f memory.dmp windows.registry.hivelist.HiveList
+
+# Registry Keys
+python vol.py -f memory.dmp windows.registry.printkey.PrintKey --key "Software\Microsoft\Windows\CurrentVersion\Run"
+```
+
+**Windows Event Logs:**
+```bash
+# Event Log Analysis
+python vol.py -f memory.dmp windows.evtlogs.EvtLogs
+```
+
+### Linux-Specific Artefakte
+
+**Process Environment:**
+```bash
+# Environment Variables
+python vol.py -f linux.dmp linux.envars.Envars
+
+# Process Arguments
+python vol.py -f linux.dmp linux.psaux.PsAux
+```
+
+**Network Connections:**
+```bash
+# Network Sockets
+python vol.py -f linux.dmp linux.netstat.Netstat
+```
+
+### macOS Memory Forensics
+
+**Darwin Kernel Structures:**
+```bash
+# Process List (macOS)
+python vol.py -f macos.dmp mac.pslist.PsList
+
+# Network Connections
+python vol.py -f macos.dmp mac.netstat.Netstat
+```
+
+## Live Analysis vs. Dead Analysis
+
+### Live Memory Analysis
+
+**Vorteile:**
+- Vollständige System-Sicht
+- Kontinuierliche Überwachung möglich
+- Interaktive Analysis-Möglichkeiten
+- Integration mit Incident Response
+
+**Tools für Live Analysis:**
+- Rekall (Live Mode)
+- WinDbg (Live Debugging)
+- GDB (Linux Live Debugging)
+- Volatility mit Live Memory Plugins
+
+**Live Analysis Workflow:**
+```bash
+# Rekall Live Analysis
+rekall --live Memory
+
+# Memory-basierte Malware Detection
+rekall> pslist
+rekall> malfind
+rekall> hollowfind
+```
+
+### Dead Memory Analysis
+
+**Vorteile:**
+- Stabile Analysis-Umgebung
+- Reproduzierbare Ergebnisse
+- Tiefere forensische Untersuchung
+- Legal-konforme Beweisführung
+
+**Typical Workflow:**
+```bash
+# 1. Memory Dump Analysis
+python vol.py -f memory.dmp windows.info.Info
+
+# 2. Process Analysis
+python vol.py -f memory.dmp windows.pslist.PsList
+python vol.py -f memory.dmp windows.pstree.PsTree
+
+# 3. Malware Detection
+python vol.py -f memory.dmp windows.malfind.Malfind
+
+# 4. Network Analysis
+python vol.py -f memory.dmp windows.netstat.NetStat
+
+# 5. Registry Analysis
+python vol.py -f memory.dmp windows.registry.hivelist.HiveList
+```
+
+## Encrypted Memory Handling
+
+### Windows BitLocker Memory
+
+BitLocker-verschlüsselte Systeme stellen besondere Herausforderungen dar:
+
+**Memory Encryption Bypass:**
+- Cold Boot Attacks auf Encryption Keys
+- DMA (Direct Memory Access) Attacks
+- Hibernation File Analysis
+
+### Full Memory Encryption (TME)
+
+Intel Total Memory Encryption (TME) verschlüsselt den gesamten Arbeitsspeicher:
+
+**Forensic Implications:**
+- Hardware-basierte Key-Extraktion erforderlich
+- Firmware-Level-Access notwendig
+- Acquisition vor Memory-Locking
+
+## Advanced Analysis Techniken
+
+### Machine Learning in Memory Forensics
+
+**Anomaly Detection:**
+```python
+# Pseudocode für ML-basierte Process Analysis
+def detect_process_anomalies(memory_dump):
+    features = extract_process_features(memory_dump)
+    # Features: Memory Permissions, API Calls, Network Connections
+    
+    model = load_trained_model('process_anomaly_detection.pkl')
+    anomalies = model.predict(features)
+    
+    return anomalies
+```
+
+### Timeline Reconstruction
+
+**Memory-basierte Timeline:**
+```bash
+# Process Creation Timeline
+python vol.py -f memory.dmp windows.pslist.PsList --output-format=timeline
+
+# File Object Timeline
+python vol.py -f memory.dmp windows.handles.Handles --object-type=File
+```
+
+### Memory Forensics Automation
+
+**Automated Analysis Framework:**
+```python
+#!/usr/bin/env python3
+class MemoryForensicsAutomation:
+    def __init__(self, memory_dump):
+        self.dump = memory_dump
+        self.results = {}
+    
+    def run_baseline_analysis(self):
+        # Basic System Information
+        self.results['info'] = self.run_volatility_plugin('windows.info.Info')
+        
+        # Process Analysis
+        self.results['processes'] = self.run_volatility_plugin('windows.pslist.PsList')
+        
+        # Malware Detection
+        self.results['malware'] = self.run_volatility_plugin('windows.malfind.Malfind')
+        
+        # Network Analysis
+        self.results['network'] = self.run_volatility_plugin('windows.netstat.NetStat')
+        
+        return self.results
+    
+    def detect_anomalies(self):
+        # Implementation für automatisierte Anomaly Detection
+        pass
+```
+
+## Häufige Herausforderungen und Lösungsansätze
+
+### Anti-Forensic Techniken
+
+**Memory Wiping:**
+- Erkennung durch Memory Allocation Patterns
+- Analyse von Memory Page Timestamps
+- Reconstruction durch Memory Slack
+
+**Process Masquerading:**
+- PE Header Validation
+- Import Address Table (IAT) Analysis
+- Code Signing Verification
+
+**Timing Attacks:**
+- Memory Acquisition Race Conditions
+- Process Termination während Acquisition
+- Kontinuierliche Monitoring-Strategien
+
+### Performance Optimierung
+
+**Large Memory Dumps:**
+```bash
+# Parallel Processing
+python vol.py -f memory.dmp --parallel=4 windows.pslist.PsList
+
+# Targeted Analysis
+python vol.py -f memory.dmp windows.pslist.PsList --pid 1234,5678
+```
+
+**Memory Usage Optimization:**
+- Streaming Analysis für große Dumps
+- Indexed Memory Access
+- Selective Plugin Execution
+
+## Tools und Framework Integration
+
+### Volatility 3 Framework
+
+**Plugin Development:**
+```python
+class CustomMalwareDetector(interfaces.plugins.PluginInterface):
+    """Custom Plugin für Advanced Malware Detection"""
+    
+    @classmethod
+    def get_requirements(cls):
+        return [requirements.TranslationLayerRequirement(name='primary'),
+                requirements.SymbolTableRequirement(name="nt_symbols")]
+    
+    def run(self):
+        # Implementation der Detection-Logik
+        pass
+```
+
+### Integration mit SIEM-Systemen
+
+**ElasticSearch Integration:**
+```python
+def export_to_elasticsearch(memory_analysis_results):
+    es = Elasticsearch(['localhost:9200'])
+    
+    for artifact in memory_analysis_results:
+        doc = {
+            'timestamp': artifact.timestamp,
+            'process_name': artifact.process_name,
+            'suspicious_score': artifact.score,
+            'detection_method': artifact.method
+        }
+        es.index(index='memory-forensics', body=doc)
+```
+
+## Best Practices und Empfehlungen
+
+### Forensic Methodology
+
+1. **Preservation First**: Memory Dump Acquisition vor anderen Aktionen
+2. **Documentation**: Vollständige Dokumentation aller Analysis-Schritte
+3. **Validation**: Cross-Referencing verschiedener Evidence Sources
+4. **Chain of Custody**: Lückenlose Beweiskette
+5. **Reproducibility**: Wiederholbare Analysis-Prozesse
+
+### Quality Assurance
+
+**Hash Verification:**
+```bash
+# MD5/SHA256 Hashes für Memory Dumps
+md5sum memory.dmp > memory.dmp.md5
+sha256sum memory.dmp > memory.dmp.sha256
+```
+
+**Analysis Documentation:**
+```markdown
+# Memory Forensics Analysis Report
+
+## System Information
+- OS Version: Windows 10 Pro 1909
+- Architecture: x64
+- Memory Size: 16GB
+- Acquisition Time: 2024-01-15 14:30:00 UTC
+
+## Tools Used
+- Volatility 3.2.0
+- Rekall 1.7.2
+- Custom Scripts: malware_detector.py
+
+## Key Findings
+1. Process Injection detected in explorer.exe (PID 1234)
+2. Unknown driver loaded: malicious.sys
+3. Network connections to suspicious IPs
+```
+
+## Fazit
+
+Memory Forensics stellt ein mächtiges Werkzeug für die Aufdeckung komplexer Angriffe dar, die traditionelle Festplatten-Forensik umgehen. Die kontinuierliche Weiterentwicklung von Angriffstechniken erfordert eine entsprechende Evolution der forensischen Methoden.
+
+**Zukünftige Entwicklungen:**
+- Hardware-basierte Memory Protection Bypass
+- Machine Learning für Automated Threat Detection
+- Cloud Memory Forensics
+- Containerized Environment Analysis
+- Real-time Memory Threat Hunting
+
+Die Beherrschung von Memory Forensics erfordert ein tiefes Verständnis von Betriebssystem-Internals, Malware-Techniken und forensischen Methoden. Kontinuierliche Weiterbildung und praktische Erfahrung sind essentiell für erfolgreiche Memory-basierte Investigations.
+
+## Weiterführende Ressourcen
+
+- **Volatility Labs Blog**: Aktuelle Research zu Memory Forensics
+- **SANS FOR508**: Advanced Incident Response und Digital Forensics
+- **Black Hat/DEF CON**: Security Conference Presentations
+- **Academic Papers**: IEEE Security & Privacy, USENIX Security
+- **Open Source Tools**: GitHub Repositories für Custom Plugins
\ No newline at end of file
diff --git a/src/content/knowledgebase/concept-network-protocols.md b/src/content/knowledgebase/concept-network-protocols.md
new file mode 100644
index 0000000..63cce30
--- /dev/null
+++ b/src/content/knowledgebase/concept-network-protocols.md
@@ -0,0 +1,517 @@
+---
+title: "Netzwerkprotokoll-Analyse für forensische Untersuchungen"
+description: "Umfassender Leitfaden zur forensischen Analyse von Netzwerkprotokollen Layer 2-7, Session-Rekonstruktion aus PCAP-Dateien, C2-Kommunikations-Pattern-Erkennung und APT-Hunting-Techniken für Incident Response."
+author: "Claude 4 Sonnett (Prompt: Mario Stöckl)"
+last_updated: 2025-08-10
+difficulty: intermediate
+categories: ["analysis", "troubleshooting", "case-study"]
+tags: ["protocol-analysis", "packet-inspection", "session-reconstruction", "c2-analysis", "traffic-patterns", "network-baseline", "payload-extraction", "anomaly-detection", "incident-response", "apt-hunting"]
+tool_name: "Network Protocols & Packet Analysis"
+related_tools: ["Wireshark", "NetworkMiner", "tcpdump"]
+published: true
+---
+
+# Netzwerkprotokoll-Analyse für forensische Untersuchungen
+
+Die forensische Analyse von Netzwerkprotokollen ist ein fundamentaler Baustein moderner Incident Response und APT-Hunting-Aktivitäten. Dieser Leitfaden vermittelt systematische Methoden zur Untersuchung von Netzwerkverkehr von Layer 2 bis Layer 7 des OSI-Modells.
+
+## Warum Netzwerkprotokoll-Forensik?
+
+In komplexen Cyberangriffen hinterlassen Angreifer Spuren in der Netzwerkkommunikation, die oft die einzigen verfügbaren Beweise darstellen. Command & Control (C2) Kommunikation, Datenexfiltration und laterale Bewegungen manifestieren sich als charakteristische Netzwerkmuster, die durch systematische Protokoll-Analyse erkennbar werden.
+
+## Voraussetzungen
+
+### Technische Kenntnisse
+- Grundverständnis des OSI-7-Schichten-Modells
+- TCP/IP-Stack-Funktionsweise
+- HTTP/HTTPS-Request/Response-Struktur
+- DNS-Query-Mechanismen
+- Grundlagen der Kryptographie (TLS/SSL)
+
+### Systemanforderungen
+- Wireshark 4.0+ oder vergleichbare Packet-Analyzer
+- Leistungsfähiges System für große PCAP-Analysen (16GB+ RAM)
+- NetworkMiner oder ähnliche Session-Rekonstruktions-Tools
+- Python 3.8+ für Automatisierungsskripte
+
+### Rechtliche Überlegungen
+- Erforderliche Genehmigungen für Netzwerk-Monitoring
+- Datenschutzbestimmungen bei der Payload-Analyse
+- Chain-of-Custody-Anforderungen für Netzwerk-Evidence
+
+## Fundamentale Protokoll-Analyse-Methodik
+
+### Layer 2 - Data Link Layer Forensik
+
+**Ethernet-Frame-Analyse für Asset-Discovery:**
+
+```bash
+# MAC-Adressen-Inventarisierung aus PCAP
+tshark -r capture.pcap -T fields -e eth.src -e eth.dst | sort -u
+```
+
+**Switch-Infrastruktur-Mapping:**
+- Spanning Tree Protocol (STP) Topologie-Rekonstruktion
+- VLAN-Segmentierung-Analyse
+- ARP-Spoofing-Detection durch MAC-IP-Binding-Inkonsistenzen
+
+**Kritische Anomalien:**
+- Unerwartete MAC-Präfixe (OUI-Analysis)
+- ARP-Reply ohne vorhergehende ARP-Request
+- Broadcast-Storm-Patterns bei DDoS-Aktivitäten
+
+### Layer 3 - Network Layer Investigation
+
+**IP-Header-Forensik für Geolocation und Routing:**
+
+```python
+# IP-Geolocation-Mapping mit Python
+import ipaddress
+from geolite2 import geolite2
+
+def analyze_ip_origins(pcap_ips):
+    reader = geolite2.reader()
+    for ip in pcap_ips:
+        if not ipaddress.ip_address(ip).is_private:
+            location = reader.get(ip)
+            print(f"{ip}: {location['country']['names']['en']}")
+```
+
+**TTL-Fingerprinting für OS-Detection:**
+- Windows: TTL 128 (typisch 128, 64, 32)
+- Linux/Unix: TTL 64
+- Cisco/Network-Equipment: TTL 255
+
+**Fragmentierungs-Analyse:**
+- Evil Fragmentation für IDS-Evasion
+- Teardrop-Attack-Patterns
+- Fragment-Overlap-Anomalien
+
+### Layer 4 - Transport Layer Forensik
+
+**TCP-Session-Rekonstruktion:**
+
+```bash
+# TCP-Streams extrahieren und analysieren
+tshark -r capture.pcap -q -z follow,tcp,ascii,0
+```
+
+**TCP-Fingerprinting-Techniken:**
+- Initial Window Size (IWS) Analysis
+- TCP-Options-Sequenz-Patterns
+- Maximum Segment Size (MSS) Charakteristika
+
+**UDP-Traffic-Anomalien:**
+- DNS-Tunneling über ungewöhnliche Record-Types
+- VoIP-Protokoll-Missbrauch für Datenexfiltration
+- TFTP-basierte Malware-Distribution
+
+## HTTP/HTTPS-Forensik für Web-basierte Angriffe
+
+### HTTP-Header-Deep-Dive
+
+**User-Agent-String-Forensik:**
+```python
+# Verdächtige User-Agent-Patterns
+suspicious_agents = [
+    "curl/",           # Command-line tools
+    "python-requests", # Scripted access
+    "Nikto",          # Vulnerability scanners
+    "sqlmap"          # SQL injection tools
+]
+```
+
+**HTTP-Method-Anomalien:**
+- PUT/DELETE-Requests auf produktiven Servern
+- TRACE-Method für XSS-Exploitation
+- Nicht-standard Methods (PATCH, OPTIONS) Analysis
+
+**Content-Type-Diskrepanzen:**
+- Executable-Content mit image/jpeg MIME-Type
+- JavaScript-Code in PDF-Dateien
+- Suspicious Content-Length vs. Actual-Payload-Size
+
+### HTTPS-Traffic-Analysis ohne Decryption
+
+**TLS-Handshake-Fingerprinting:**
+```bash
+# TLS-Version und Cipher-Suite-Analyse
+tshark -r capture.pcap -Y "tls.handshake.type == 1" \
+       -T fields -e tls.handshake.version -e tls.handshake.ciphersuites
+```
+
+**Certificate-Chain-Investigation:**
+- Self-signed Certificate-Anomalien
+- Certificate-Transparency-Log-Validation
+- Subject Alternative Name (SAN) Missbrauch
+
+**Encrypted-Traffic-Patterns:**
+- Packet-Size-Distribution-Analysis
+- Inter-arrival-Time-Patterns
+- Burst-Communication vs. Steady-State-Traffic
+
+## DNS-Forensik und Tunneling-Detection
+
+### DNS-Query-Pattern-Analysis
+
+**DNS-Tunneling-Indicators:**
+```python
+# DNS-Query-Length-Distribution-Analysis
+def analyze_dns_queries(pcap_file):
+    queries = extract_dns_queries(pcap_file)
+    avg_length = sum(len(q) for q in queries) / len(queries)
+    
+    # Normal DNS: 15-30 chars, Tunneling: 50+ chars
+    if avg_length > 50:
+        return "POTENTIAL_TUNNELING"
+```
+
+**Subdomain-Enumeration-Detection:**
+- Excessive NXDOMAIN-Responses
+- Sequential-Subdomain-Queries
+- High-Entropy-Subdomain-Names
+
+**DNS-over-HTTPS (DoH) Investigation:**
+- DoH-Provider-Identification (Cloudflare, Google, Quad9)
+- Encrypted-DNS-vs-Clear-DNS-Ratio-Analysis
+- Bootstrap-DNS-Query-Patterns
+
+## Command & Control (C2) Communication-Patterns
+
+### C2-Channel-Identification
+
+**HTTP-basierte C2-Kommunikation:**
+```bash
+# Beaconing-Pattern-Detection
+tshark -r capture.pcap -T fields -e frame.time_epoch -e ip.dst \
+       -Y "http" | awk 'script für regelmäßige Intervalle'
+```
+
+**Timing-Analysis für Beaconing:**
+- Jitter-Analyse bei Sleep-Intervallen
+- Callback-Frequency-Patterns
+- Network-Outage-Response-Behavior
+
+**Payload-Obfuscation-Techniques:**
+- Base64-encoded Commands in HTTP-Bodies
+- Steganographie in Bilddateien
+- JSON/XML-Structure-Abuse für Command-Transport
+
+### Advanced Persistent Threat (APT) Network-Signatures
+
+**Long-Duration-Connection-Analysis:**
+```python
+# Langzeit-Verbindungs-Identifikation
+def find_persistent_connections(pcap_data):
+    for session in tcp_sessions:
+        duration = session.end_time - session.start_time
+        if duration > timedelta(hours=24):
+            analyze_session_behavior(session)
+```
+
+**Multi-Stage-Payload-Delivery:**
+- Initial-Compromise-Vector-Analysis
+- Secondary-Payload-Download-Patterns
+- Lateral-Movement-Network-Signatures
+
+## Protokoll-Anomalie-Detection-Algorithmen
+
+### Statistical-Baseline-Establishment
+
+**Traffic-Volume-Baselines:**
+```python
+# Netzwerk-Baseline-Erstellung
+def establish_baseline(historical_data):
+    baseline = {
+        'avg_bandwidth': calculate_average_bps(historical_data),
+        'peak_hours': identify_peak_traffic_windows(historical_data),
+        'protocol_distribution': analyze_protocol_ratios(historical_data)
+    }
+    return baseline
+```
+
+**Port-Usage-Pattern-Analysis:**
+- Unexpected-Port-Combinations
+- High-Port-Range-Communication (> 32768)
+- Service-Port-Mismatches (HTTP on Port 443 without TLS)
+
+### Machine-Learning-Enhanced-Detection
+
+**Traffic-Classification-Models:**
+- Protocol-Identification via Payload-Analysis
+- Encrypted-Traffic-Classification
+- Anomaly-Score-Calculation für Unknown-Traffic
+
+## Session-Rekonstruktion und Payload-Extraktion
+
+### TCP-Stream-Reassembly
+
+**Bidirectional-Communication-Timeline:**
+```bash
+# Vollständige Session-Rekonstruktion
+mkdir session_analysis
+cd session_analysis
+
+# TCP-Streams einzeln extrahieren
+for stream in $(tshark -r ../capture.pcap -T fields -e tcp.stream | sort -u); do
+    tshark -r ../capture.pcap -q -z follow,tcp,raw,$stream > stream_$stream.raw
+done
+```
+
+**File-Carving aus Network-Streams:**
+- HTTP-File-Download-Reconstruction
+- Email-Attachment-Extraction via SMTP/POP3
+- FTP-Data-Channel-File-Recovery
+
+### Application-Layer-Protocol-Parsing
+
+**Custom-Protocol-Analysis:**
+```python
+# Proprietary-Protocol-Reverse-Engineering
+def analyze_custom_protocol(payload):
+    # Header-Structure-Identification
+    if len(payload) > 8:
+        magic_bytes = payload[:4]
+        length_field = struct.unpack('>I', payload[4:8])[0]
+        
+        if validate_structure(magic_bytes, length_field, payload):
+            return parse_protocol_fields(payload)
+```
+
+## Verschlüsselte Protokoll-Forensik
+
+### TLS/SSL-Traffic-Analysis
+
+**Certificate-Chain-Validation:**
+```bash
+# Certificate-Extraktion aus PCAP
+tshark -r capture.pcap -Y "tls.handshake.certificate" \
+       -T fields -e tls.handshake.certificate > certificates.hex
+
+# Certificate-Parsing
+xxd -r -p certificates.hex | openssl x509 -inform DER -text
+```
+
+**TLS-Version-Downgrade-Attacks:**
+- Forced-SSLv3-Negotiation-Detection
+- Weak-Cipher-Suite-Selection-Patterns
+- Certificate-Pinning-Bypass-Indicators
+
+### VPN-Traffic-Characterization
+
+**VPN-Protocol-Identification:**
+- OpenVPN: UDP Port 1194, specific packet-patterns
+- IPSec: ESP (Protocol 50), IKE (UDP 500)
+- WireGuard: UDP mit characteristic handshake-patterns
+
+**VPN-Tunnel-Analysis:**
+```python
+# VPN-Endpoint-Discovery
+def identify_vpn_endpoints(pcap_data):
+    potential_endpoints = []
+    for packet in pcap_data:
+        if detect_vpn_signature(packet):
+            potential_endpoints.append(packet.src_ip)
+    return analyze_endpoint_patterns(potential_endpoints)
+```
+
+## Häufige Herausforderungen und Troubleshooting
+
+### Performance-Optimierung bei großen PCAP-Dateien
+
+**Memory-Management:**
+```bash
+# Große PCAP-Dateien in kleinere Segmente aufteilen
+editcap -c 100000 large_capture.pcap segment.pcap
+
+# Zeitbasierte Segmentierung
+editcap -A "2024-01-01 00:00:00" -B "2024-01-01 01:00:00" \
+        large_capture.pcap hour_segment.pcap
+```
+
+**Selective-Filtering:**
+```bash
+# Nur relevanten Traffic extrahieren
+tshark -r large_capture.pcap -w filtered.pcap \
+       -Y "ip.addr == 192.168.1.100 or dns or http"
+```
+
+### False-Positive-Reduction
+
+**Legitimate-Traffic-Whitelisting:**
+- Corporate-Application-Signatures
+- Known-Good-Certificate-Authorities
+- Approved-Remote-Access-Solutions
+
+**Context-Aware-Analysis:**
+```python
+# Business-Context-Integration
+def validate_alert(network_event, business_context):
+    if is_maintenance_window(network_event.timestamp):
+        return False
+    if is_authorized_admin(network_event.source_ip):
+        return validate_admin_action(network_event)
+    return True
+```
+
+## Praktische Anwendungsszenarien
+
+### Szenario 1: Data Exfiltration Detection
+
+**Ausgangslage:** Verdacht auf Datendiebstahl aus dem Unternehmensnetzwerk
+
+**Analyse-Workflow:**
+1. **Baseline-Establishment:** Normale ausgehende Datenvolumen ermitteln
+2. **Spike-Detection:** Ungewöhnlich hohe Upload-Aktivitäten identifizieren
+3. **Destination-Analysis:** Externe Ziele der Datenübertragungen
+4. **Content-Classification:** Art der übertragenen Daten (soweit möglich)
+
+```bash
+# Ausgehende Datenvolumen-Analyse
+tshark -r capture.pcap -q -z io,stat,300 \
+       -Y "ip.src == 192.168.0.0/16 and ip.dst != 192.168.0.0/16"
+```
+
+### Szenario 2: APT-Lateral-Movement-Investigation
+
+**Ausgangslage:** Kompromittierter Host, Verdacht auf laterale Bewegung
+
+**Detection-Methoden:**
+- SMB-Authentication-Patterns (Pass-the-Hash-Attacks)
+- RDP-Session-Establishment-Chains
+- WMI/PowerShell-Remote-Execution-Signatures
+
+```python
+# Lateral-Movement-Timeline-Construction
+def construct_movement_timeline(network_data):
+    timeline = []
+    for connection in extract_internal_connections(network_data):
+        if detect_admin_protocols(connection):
+            timeline.append({
+                'timestamp': connection.start_time,
+                'source': connection.src_ip,
+                'target': connection.dst_ip,
+                'protocol': connection.protocol,
+                'confidence': calculate_suspicion_score(connection)
+            })
+    return sort_by_timestamp(timeline)
+```
+
+### Szenario 3: Malware C2 Communication Analysis
+
+**Ausgangslage:** Identifizierte Malware-Infection, C2-Channel-Mapping erforderlich
+
+**Systematic C2-Analysis:**
+1. **Beaconing-Pattern-Identification**
+2. **C2-Server-Geolocation**
+3. **Command-Structure-Reverse-Engineering**
+4. **Kill-Chain-Reconstruction**
+
+```bash
+# C2-Communication-Timeline
+tshark -r malware_capture.pcap -T fields \
+       -e frame.time -e ip.src -e ip.dst -e tcp.dstport \
+       -Y "ip.src == <infected_host>" | \
+       awk '{print $1, $4}' | sort | uniq -c
+```
+
+## Erweiterte Analyse-Techniken
+
+### Protocol-State-Machine-Analysis
+
+**TCP-State-Tracking:**
+```python
+class TCPStateAnalyzer:
+    def __init__(self):
+        self.connections = {}
+    
+    def process_packet(self, packet):
+        key = (packet.src_ip, packet.src_port, packet.dst_ip, packet.dst_port)
+        
+        if key not in self.connections:
+            self.connections[key] = TCPConnection()
+        
+        conn = self.connections[key]
+        conn.update_state(packet.tcp_flags)
+        
+        if conn.is_anomalous():
+            self.flag_suspicious_connection(key, conn)
+```
+
+**Application-Protocol-State-Validation:**
+- HTTP-Request/Response-Pairing-Validation
+- DNS-Query/Response-Correlation
+- SMTP-Session-Command-Sequence-Analysis
+
+### Geospatial-Network-Analysis
+
+**IP-Geolocation-Correlation:**
+```python
+# Geographische Anomalie-Detection
+def detect_geographic_anomalies(connections):
+    for conn in connections:
+        src_country = geolocate_ip(conn.src_ip)
+        dst_country = geolocate_ip(conn.dst_ip)
+        
+        if calculate_distance(src_country, dst_country) > 10000:  # km
+            if not is_known_global_service(conn.dst_ip):
+                flag_suspicious_connection(conn)
+```
+
+## Automatisierung und Tool-Integration
+
+### SIEM-Integration
+
+**Log-Format-Standardization:**
+```python
+# Network-Events zu SIEM-Format
+def convert_to_siem_format(network_event):
+    return {
+        'timestamp': network_event.time_iso,
+        'event_type': 'network_connection',
+        'source_ip': network_event.src_ip,
+        'destination_ip': network_event.dst_ip,
+        'protocol': network_event.protocol,
+        'risk_score': calculate_risk_score(network_event),
+        'indicators': extract_iocs(network_event)
+    }
+```
+
+### Threat-Intelligence-Integration
+
+**IOC-Matching:**
+```bash
+# Threat-Feed-Integration
+curl -s "https://threatfeed.example.com/api/ips" | \
+tee threat_ips.txt
+
+tshark -r capture.pcap -T fields -e ip.dst | \
+sort -u | \
+grep -f threat_ips.txt
+```
+
+## Nächste Schritte und Vertiefung
+
+### Weiterführende Analyse-Techniken
+- **Behavioral-Analysis:** Machine-Learning-basierte Anomalie-Detection
+- **Graph-Analysis:** Netzwerk-Relationship-Mapping
+- **Temporal-Analysis:** Time-Series-basierte Pattern-Recognition
+
+### Spezialisierung-Richtungen
+- **Cloud-Network-Forensics:** AWS VPC Flow Logs, Azure NSG Analysis
+- **IoT-Network-Analysis:** Constrained-Device-Communication-Patterns
+- **Industrial-Network-Security:** SCADA/Modbus-Protocol-Forensics
+
+### Tool-Ecosystem-Erweiterung
+- **Zeek (Bro):** Scriptable Network Security Monitor
+- **Suricata:** IDS/IPS mit Network-Forensik-Capabilities
+- **Moloch:** Full-Packet-Capture und Search-Platform
+
+Die systematische Netzwerkprotokoll-Analyse bildet das Fundament moderner Cyber-Forensik. Durch die Kombination von Deep-Protocol-Knowledge, statistischer Analyse und Threat-Intelligence entsteht ein mächtiges Arsenal für die Aufdeckung und Untersuchung von Cyberangriffen.
+
+**Empfohlene Übungen:**
+1. Analysieren Sie einen selbst erzeugten Netzwerk-Capture mit bekanntem "böswilligem" Traffic
+2. Implementieren Sie ein automatisiertes C2-Detection-Script
+3. Führen Sie eine komplette APT-Simulation durch und dokumentieren Sie die Netzwerk-Artefakte
+
+Die kontinuierliche Weiterentwicklung von Angriffstechniken erfordert permanente Aktualisierung der Analyse-Methoden. Bleiben Sie über aktuelle Threat-Research und neue Protocol-Exploitation-Techniques informiert.
\ No newline at end of file
diff --git a/src/content/knowledgebase/concept-regular-expressions-regex.md b/src/content/knowledgebase/concept-regular-expressions-regex.md
new file mode 100644
index 0000000..e66eae1
--- /dev/null
+++ b/src/content/knowledgebase/concept-regular-expressions-regex.md
@@ -0,0 +1,556 @@
+---
+title: "Regular Expressions in der Digitalen Forensik: Vom Grundmuster zur Beweisextraktion"
+description: "Umfassender Leitfaden für Regex-Anwendungen in der forensischen Analyse: IP-Adressen, E-Mails, Hashes und komplexe Logparser-Patterns für effiziente Beweissammlung"
+author: "Claude 4 Sonnett (Prompt: Mario Stöckl)"
+last_updated: 2025-08-10
+difficulty: intermediate
+categories: ["analysis", "automation", "log-analysis"]
+tags: ["regex", "pattern-matching", "log-analysis", "data-extraction", "text-processing", "automation", "yara-rules", "grep", "powershell", "python"]
+tool_name: "Regular Expressions (Regex)"
+related_tools: ["YARA", "Grep", "PowerShell", "Python"]
+published: true
+---
+
+# Regular Expressions in der Digitalen Forensik: Vom Grundmuster zur Beweisextraktion
+
+Regular Expressions (Regex) sind das Schweizer Taschenmesser der digitalen Forensik. Diese universelle Mustererkennungssprache ermöglicht es Forensikern, komplexe Textsuchen durchzuführen, relevante Daten aus Terabytes von Logs zu extrahieren und Beweise systematisch zu identifizieren. Von der einfachen IP-Adressen-Suche bis zur komplexen Malware-Signaturerstellung - Regex-Kenntnisse unterscheiden oft einen guten von einem großartigen Forensiker.
+
+## Warum Regex in der Forensik unverzichtbar ist
+
+In modernen Untersuchungen konfrontieren uns massive Datenmengen: Gigabytes von Logfiles, Speicherabbilder, Netzwerkverkehr und Dateisysteme mit Millionen von Einträgen. Manuelle Durchsuchung ist unmöglich - hier kommt Regex ins Spiel:
+
+- **Präzise Mustersuche**: Findet spezifische Datenformate (IP-Adressen, E-Mails, Hashes) in unstrukturierten Texten
+- **Automatisierung**: Ermöglicht Skripterstellung für wiederkehrende Analysemuster
+- **Tool-Integration**: Kernfunktionalität in allen Major-Forensik-Tools
+- **Effizienzsteigerung**: Reduziert Analysezeit von Stunden auf Minuten
+
+## Forensik-relevante Regex-Grundlagen
+
+### Grundlegende Metacharakter
+
+```regex
+.       # Beliebiges Zeichen (außer Newline)
+*       # 0 oder mehr Wiederholungen des vorherigen Elements
++       # 1 oder mehr Wiederholungen
+?       # 0 oder 1 Wiederholung (optional)
+^       # Zeilenanfang
+$       # Zeilenende
+[]      # Zeichenklasse
+()      # Gruppierung
+|       # ODER-Verknüpfung
+\       # Escape-Zeichen
+```
+
+### Quantifizierer für präzise Treffer
+
+```regex
+{n}     # Exakt n Wiederholungen
+{n,}    # Mindestens n Wiederholungen
+{n,m}   # Zwischen n und m Wiederholungen
+{,m}    # Maximal m Wiederholungen
+```
+
+### Zeichenklassen für strukturierte Daten
+
+```regex
+\d      # Ziffer (0-9)
+\w      # Wort-Zeichen (a-z, A-Z, 0-9, _)
+\s      # Whitespace (Leerzeichen, Tab, Newline)
+\D      # Nicht-Ziffer
+\W      # Nicht-Wort-Zeichen
+\S      # Nicht-Whitespace
+[a-z]   # Kleinbuchstaben
+[A-Z]   # Großbuchstaben
+[0-9]   # Ziffern
+[^abc]  # Alles außer a, b, c
+```
+
+## Forensische Standardmuster
+
+### IP-Adressen (IPv4)
+
+```regex
+# Basis-Pattern (weniger präzise)
+\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}
+
+# Präzise IPv4-Validierung
+^(?:(?:25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)\.){3}(?:25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)$
+
+# Praktisches Pattern für Log-Analyse
+(?:(?:25[0-5]|2[0-4]\d|[01]?\d\d?)\.){3}(?:25[0-5]|2[0-4]\d|[01]?\d\d?)
+```
+
+**Anwendungsbeispiel**: Extraktion aller IP-Adressen aus IIS-Logs:
+```bash
+grep -oE '(?:(?:25[0-5]|2[0-4]\d|[01]?\d\d?)\.){3}(?:25[0-5]|2[0-4]\d|[01]?\d\d?)' access.log | sort | uniq -c | sort -nr
+```
+
+### E-Mail-Adressen
+
+```regex
+# Einfaches Pattern für schnelle Suche
+[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}
+
+# RFC-konforme E-Mail (vereinfacht)
+^[a-zA-Z0-9.!#$%&'*+/=?^_`{|}~-]+@[a-zA-Z0-9](?:[a-zA-Z0-9-]{0,61}[a-zA-Z0-9])?(?:\.[a-zA-Z0-9](?:[a-zA-Z0-9-]{0,61}[a-zA-Z0-9])?)*$
+
+# Für Forensik optimiert (weniger strikt)
+\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b
+```
+
+### Hash-Werte
+
+```regex
+# MD5 (32 Hexadezimalzeichen)
+\b[a-fA-F0-9]{32}\b
+
+# SHA-1 (40 Hexadezimalzeichen)
+\b[a-fA-F0-9]{40}\b
+
+# SHA-256 (64 Hexadezimalzeichen)
+\b[a-fA-F0-9]{64}\b
+
+# Universelles Hash-Pattern
+\b[a-fA-F0-9]{32,64}\b
+```
+
+### Bitcoin-Adressen
+
+```regex
+# Legacy Bitcoin-Adressen (P2PKH und P2SH)
+\b[13][a-km-zA-HJ-NP-Z1-9]{25,34}\b
+
+# Bech32 (SegWit) Adressen
+\bbc1[a-z0-9]{39,59}\b
+
+# Kombiniert
+\b(?:[13][a-km-zA-HJ-NP-Z1-9]{25,34}|bc1[a-z0-9]{39,59})\b
+```
+
+### Windows-Dateipfade
+
+```regex
+# Vollständiger Windows-Pfad
+^[a-zA-Z]:\\(?:[^\\/:*?"<>|\r\n]+\\)*[^\\/:*?"<>|\r\n]*$
+
+# UNC-Pfade
+^\\\\[^\\]+\\[^\\]+(?:\\[^\\]*)*$
+
+# Für Log-Parsing (flexibler)
+[a-zA-Z]:\\[^"\s<>|]*
+```
+
+### Kreditkartennummern
+
+```regex
+# Visa (13-19 Ziffern, beginnt mit 4)
+4[0-9]{12,18}
+
+# MasterCard (16 Ziffern, beginnt mit 5)
+5[1-5][0-9]{14}
+
+# American Express (15 Ziffern, beginnt mit 34 oder 37)
+3[47][0-9]{13}
+
+# Universell (mit optionalen Trennzeichen)
+(?:\d{4}[-\s]?){3,4}\d{4}
+```
+
+## Tool-spezifische Regex-Implementierungen
+
+### PowerShell-Integration
+
+```powershell
+# Suche nach IP-Adressen in Eventlogs
+Get-WinEvent -LogName Security | Where-Object {
+    $_.Message -match '\b(?:\d{1,3}\.){3}\d{1,3}\b'
+} | Select-Object TimeCreated, Id, Message
+
+# E-Mail-Extraktion aus Speicherabbild
+Select-String -Path "memdump.raw" -Pattern '[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}' -AllMatches
+
+# Hash-Werte aus Malware-Samples
+Get-ChildItem -Recurse | Get-FileHash | Where-Object {
+    $_.Hash -match '^[a-fA-F0-9]{64}$'
+}
+```
+
+### Grep-Anwendungen
+
+```bash
+# Verdächtige ausführbare Dateien
+grep -r -E '\.(exe|dll|scr|bat|cmd)$' /mnt/evidence/
+
+# Zeitstempel-Extraktion (ISO 8601)
+grep -oE '\d{4}-\d{2}-\d{2}[T ]\d{2}:\d{2}:\d{2}' application.log
+
+# Base64-kodierte Daten
+grep -oE '[A-Za-z0-9+/]{20,}={0,2}' suspicious.txt
+
+# Windows-Ereignis-IDs
+grep -E 'Event ID: (4624|4625|4648|4656)' security.log
+```
+
+### Python-Implementierung
+
+```python
+import re
+import hashlib
+
+# IP-Adressen mit Kontext extrahieren
+def extract_ips_with_context(text, context_chars=50):
+    ip_pattern = r'\b(?:\d{1,3}\.){3}\d{1,3}\b'
+    matches = []
+    
+    for match in re.finditer(ip_pattern, text):
+        start = max(0, match.start() - context_chars)
+        end = min(len(text), match.end() + context_chars)
+        context = text[start:end]
+        matches.append({
+            'ip': match.group(),
+            'position': match.start(),
+            'context': context
+        })
+    
+    return matches
+
+# Malware-Signaturen generieren
+def generate_yara_strings(binary_data, min_length=10):
+    # Suche nach druckbaren ASCII-Strings
+    ascii_pattern = rb'[ -~]{' + str(min_length).encode() + rb',}'
+    strings = re.findall(ascii_pattern, binary_data)
+    
+    yara_strings = []
+    for i, string in enumerate(strings[:20]):  # Erste 20 Strings
+        # Escape problematische Zeichen
+        escaped = string.decode('ascii').replace('\\', '\\\\').replace('"', '\\"')
+        yara_strings.append(f'$s{i} = "{escaped}"')
+    
+    return yara_strings
+```
+
+## YARA-Rules mit Regex
+
+```yara
+rule SuspiciousEmailPattern {
+    strings:
+        $email = /[a-zA-Z0-9._%+-]+@(tempmail|guerrillamail|10minutemail)\.(com|net|org)/ nocase
+        $bitcoin = /\b[13][a-km-zA-HJ-NP-Z1-9]{25,34}\b/
+        $ransom_msg = /your files have been encrypted/i
+    
+    condition:
+        $email and ($bitcoin or $ransom_msg)
+}
+
+rule LogAnalysisPattern {
+    strings:
+        $failed_login = /Failed login.*from\s+(\d{1,3}\.){3}\d{1,3}/
+        $brute_force = /authentication failure.*rhost=(\d{1,3}\.){3}\d{1,3}/
+        $suspicious_ua = /User-Agent:.*(?:sqlmap|nikto|nmap|masscan)/i
+    
+    condition:
+        any of them
+}
+```
+
+## Performance-Optimierung und Fallstricke
+
+### Catastrophic Backtracking vermeiden
+
+**Problematisch**:
+```regex
+(a+)+b     # Exponentieller Zeitverbrauch bei "aaaa...c"
+(.*)*      # Verschachtelte Quantifizierer
+```
+
+**Optimiert**:
+```regex
+a+b        # Atomare Gruppierung
+[^b]*b     # Negierte Zeichenklasse statt .*
+```
+
+### Anker für Effizienz nutzen
+
+```regex
+# Langsam
+\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}
+
+# Schneller mit Wortgrenzen
+\b\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}\b
+
+# Am schnellsten für Zeilensuche
+^\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}$
+```
+
+### Compiled Patterns verwenden
+
+```python
+import re
+
+# Einmal kompilieren, oft verwenden
+ip_pattern = re.compile(r'\b(?:\d{1,3}\.){3}\d{1,3}\b')
+email_pattern = re.compile(r'[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}')
+
+def analyze_log_file(filepath):
+    with open(filepath, 'r', encoding='utf-8', errors='ignore') as f:
+        content = f.read()
+        
+    ips = ip_pattern.findall(content)
+    emails = email_pattern.findall(content)
+    
+    return ips, emails
+```
+
+## Praktische Forensik-Szenarien
+
+### Incident Response: Lateral Movement Detection
+
+```bash
+# Suche nach PsExec-Aktivitäten
+grep -E 'PSEXESVC.*started|PsExec.*\\\\[^\\]+\\' security.log
+
+# Pass-the-Hash Angriffe
+grep -E 'Logon Type:\s+9.*NTLM.*[0-9a-fA-F]{32}' security.log
+
+# WMI-basierte Ausführung
+grep -E 'WmiPrvSE.*ExecuteShellCommand|wmic.*process.*call.*create' system.log
+```
+
+### Malware-Analyse: C2-Kommunikation
+
+```python
+# Domain Generation Algorithm (DGA) Detection
+dga_pattern = re.compile(r'\b[a-z]{8,20}\.(com|net|org|info)\b')
+
+def detect_suspicious_domains(pcap_text):
+    # Extrahiere DNS-Queries
+    dns_pattern = r'DNS.*query.*?([a-zA-Z0-9.-]+\.[a-zA-Z]{2,})'
+    domains = re.findall(dns_pattern, pcap_text)
+    
+    suspicious = []
+    for domain in domains:
+        # Prüfe auf DGA-Charakteristika
+        if dga_pattern.match(domain.lower()):
+            # Zusätzliche Heuristiken
+            vowel_ratio = len(re.findall(r'[aeiou]', domain.lower())) / len(domain)
+            if vowel_ratio < 0.2:  # Wenige Vokale = verdächtig
+                suspicious.append(domain)
+    
+    return suspicious
+```
+
+### Data Exfiltration: Ungewöhnliche Datenübertragungen
+
+```regex
+# Base64-kodierte Daten in URLs
+[?&]data=([A-Za-z0-9+/]{4})*([A-Za-z0-9+/]{2}==|[A-Za-z0-9+/]{3}=)?
+
+# DNS-Tunneling (ungewöhnlich lange Subdomains)
+\b[a-z0-9]{20,}\.[a-z0-9.-]+\.[a-z]{2,}\b
+
+# Hex-kodierte Dateninhalte
+[?&]payload=[0-9a-fA-F]{40,}
+```
+
+## Debugging und Testing
+
+### Online-Tools für Regex-Entwicklung
+
+1. **regex101.com**: Interaktive Regex-Entwicklung mit Erklärungen
+2. **regexr.com**: Visuelle Regex-Darstellung
+3. **regexpal.com**: Schnelle Tests ohne Anmeldung
+
+### Regex-Validierung in der Praxis
+
+```python
+import re
+
+def validate_regex_pattern(pattern, test_cases):
+    """
+    Validiert Regex-Pattern gegen bekannte Test-Cases
+    """
+    try:
+        compiled = re.compile(pattern)
+    except re.error as e:
+        return False, f"Regex-Syntax-Fehler: {e}"
+    
+    results = []
+    for test_input, expected in test_cases:
+        match = compiled.search(test_input)
+        found = match.group() if match else None
+        results.append({
+            'input': test_input,
+            'expected': expected,
+            'found': found,
+            'correct': found == expected
+        })
+    
+    return True, results
+
+# Test-Cases für IP-Pattern
+ip_tests = [
+    ('192.168.1.1', '192.168.1.1'),
+    ('999.999.999.999', None),  # Ungültige IP
+    ('text 10.0.0.1 more text', '10.0.0.1'),
+    ('no.ip.here', None)
+]
+
+pattern = r'\b(?:(?:25[0-5]|2[0-4]\d|[01]?\d\d?)\.){3}(?:25[0-5]|2[0-4]\d|[01]?\d\d?)\b'
+valid, results = validate_regex_pattern(pattern, ip_tests)
+```
+
+## Häufige Fehler und Lösungen
+
+### Problem: Gierige vs. nicht-gierige Quantifizierer
+
+```regex
+# Problematisch: Gierig
+<.*>            # Matched "<tag>content</tag>" komplett
+
+# Lösung: Nicht-gierig
+<.*?>           # Matched nur "<tag>"
+
+# Alternative: Spezifisch
+<[^>]*>         # Matched keine ">" innerhalb
+```
+
+### Problem: Unbeabsichtigte Metacharakter
+
+```regex
+# Falsch: . als Literalzeichen gemeint
+192.168.1.1     # Matched auch "192x168x1x1"
+
+# Richtig: Escape von Metacharaktern
+192\.168\.1\.1  # Matched nur echte IP
+```
+
+### Problem: Fehlende Wortgrenzen
+
+```regex
+# Problematisch: Matcht Teilstrings
+\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}  # Matched "1192.168.1.10"
+
+# Lösung: Wortgrenzen verwenden
+\b\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}\b  # Nur vollständige IPs
+```
+
+## Integration in Forensik-Workflows
+
+### Automatisierte Triage-Scripts
+
+```bash
+#!/bin/bash
+# forensic_triage.sh - Automatisierte erste Analyse
+
+LOG_DIR="/evidence/logs"
+OUTPUT_DIR="/analysis/regex_results"
+
+# IP-Adressen extrahieren und häufigste finden
+echo "=== IP-Analyse ===" > $OUTPUT_DIR/summary.txt
+find $LOG_DIR -name "*.log" -exec grep -h -oE '\b(?:\d{1,3}\.){3}\d{1,3}\b' {} \; | \
+    sort | uniq -c | sort -nr | head -20 >> $OUTPUT_DIR/summary.txt
+
+# E-Mail-Adressen sammeln
+echo -e "\n=== E-Mail-Adressen ===" >> $OUTPUT_DIR/summary.txt
+find $LOG_DIR -name "*.log" -exec grep -h -oE '[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}' {} \; | \
+    sort | uniq >> $OUTPUT_DIR/summary.txt
+
+# Verdächtige Prozessnamen
+echo -e "\n=== Verdächtige Prozesse ===" >> $OUTPUT_DIR/summary.txt
+find $LOG_DIR -name "*.log" -exec grep -h -iE '(powershell|cmd|wmic|psexec|mimikatz)' {} \; | \
+    head -50 >> $OUTPUT_DIR/summary.txt
+```
+
+### PowerShell-Module für wiederkehrende Aufgaben
+
+```powershell
+function Get-ForensicPatterns {
+    param(
+        [string]$Path,
+        [string[]]$Patterns = @(
+            '\b(?:\d{1,3}\.){3}\d{1,3}\b',  # IP-Adressen
+            '[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}',  # E-Mails
+            '\b[a-fA-F0-9]{32,64}\b'  # Hash-Werte
+        )
+    )
+    
+    $results = @{}
+    
+    foreach ($pattern in $Patterns) {
+        $matches = Select-String -Path $Path -Pattern $pattern -AllMatches
+        $results[$pattern] = $matches | ForEach-Object {
+            [PSCustomObject]@{
+                File = $_.Filename
+                Line = $_.LineNumber
+                Match = $_.Matches.Value
+                Context = $_.Line
+            }
+        }
+    }
+    
+    return $results
+}
+```
+
+## Weiterführende Techniken
+
+### Lookahead und Lookbehind
+
+```regex
+# Positive Lookahead: Password gefolgt von Ziffer
+password(?=.*\d)
+
+# Negative Lookahead: IP nicht in private ranges
+(?!(?:10\.|192\.168\.|172\.(?:1[6-9]|2[0-9]|3[01])\.))(?:\d{1,3}\.){3}\d{1,3}
+
+# Positive Lookbehind: Zahl nach "Port:"
+(?<=Port:)\d+
+
+# Negative Lookbehind: Nicht nach "Comment:"
+(?<!Comment:).+@.+\..+
+```
+
+### Named Capture Groups
+
+```python
+import re
+
+# Strukturierte Log-Parsing
+log_pattern = re.compile(
+    r'(?P<timestamp>\d{4}-\d{2}-\d{2} \d{2}:\d{2}:\d{2}) '
+    r'\[(?P<level>\w+)\] '
+    r'(?P<source>\w+): '
+    r'(?P<message>.*)'
+)
+
+def parse_log_entry(line):
+    match = log_pattern.match(line)
+    if match:
+        return match.groupdict()
+    return None
+
+# Verwendung
+log_line = "2024-01-15 14:30:25 [ERROR] auth: Failed login from 192.168.1.100"
+parsed = parse_log_entry(log_line)
+# Result: {'timestamp': '2024-01-15 14:30:25', 'level': 'ERROR', 
+#          'source': 'auth', 'message': 'Failed login from 192.168.1.100'}
+```
+
+## Nächste Schritte
+
+Nach diesem umfassenden Überblick können Sie:
+
+1. **Praktische Übung**: Implementieren Sie die vorgestellten Patterns in Ihren aktuellen Untersuchungen
+2. **Tool-Integration**: Integrieren Sie Regex in Ihre bevorzugten Forensik-Tools
+3. **Automatisierung**: Entwickeln Sie Scripts für wiederkehrende Analysemuster
+4. **Spezialisierung**: Vertiefen Sie sich in tool-spezifische Regex-Implementierungen
+5. **Community**: Teilen Sie Ihre Patterns und lernen Sie von anderen Forensikern
+
+### Weiterführende Ressourcen
+
+- **SANS Regex Cheat Sheet**: Kompakte Referenz für Forensiker
+- **RegexBuddy**: Professionelle Regex-Entwicklungsumgebung
+- **Python re-Modul Dokumentation**: Detaillierte Syntax-Referenz
+- **YARA-Rules Repository**: Sammlung forensik-relevanter Regex-Patterns
+
+Regular Expressions sind ein mächtiges Werkzeug, das Zeit spart und die Präzision forensischer Analysen erhöht. Die Investition in solide Regex-Kenntnisse zahlt sich in jeder Untersuchung aus und ermöglicht es, komplexe Muster zu erkennen, die manuell übersehen werden würden.
\ No newline at end of file
diff --git a/src/content/knowledgebase/concept-sql.md b/src/content/knowledgebase/concept-sql.md
new file mode 100644
index 0000000..7dc8579
--- /dev/null
+++ b/src/content/knowledgebase/concept-sql.md
@@ -0,0 +1,770 @@
+---
+title: "SQL in der digitalen Forensik: Von SQLite-Datenbanken zur Timeline-Analyse"
+description: "Umfassender Leitfaden für SQL-basierte Forensik-Analysen: SQLite-Datenbanken untersuchen, Timeline-Rekonstruktion durchführen, mobile App-Daten analysieren und komplexe Korrelationen aufdecken."
+author: "Claude 4 Sonnett (Prompt: Mario Stöckl)"
+last_updated: 2025-08-10
+difficulty: intermediate
+categories: ["analysis", "configuration", "case-study"]
+tags: ["sqlite-viewer", "correlation-engine", "mobile-app-data", "browser-history", "data-extraction", "timeline-queries", "join-operations", "aggregate-analysis", "wal-analysis", "python-integration"]
+tool_name: "SQL"
+related_tools: ["DB Browser for SQLite", "Autopsy", "Cellebrite UFED"]
+published: true
+---
+
+# SQL in der digitalen Forensik: Von SQLite-Datenbanken zur Timeline-Analyse
+
+SQL (Structured Query Language) ist eine der mächtigsten und unterschätztesten Fähigkeiten in der modernen digitalen Forensik. Während viele Ermittler auf GUI-basierte Tools setzen, ermöglicht SQL direkten Zugriff auf Rohdaten und komplexe Analysen, die mit herkömmlichen Tools unmöglich wären.
+
+## Warum SQL in der Forensik unverzichtbar ist
+
+### SQLite dominiert die mobile Forensik
+- **WhatsApp-Chats**: Nachrichten, Metadaten, gelöschte Inhalte
+- **Browser-History**: Zeitstempel, Besuchshäufigkeit, Suchverläufe  
+- **App-Daten**: Standortdaten, Nutzerverhalten, Cache-Inhalte
+- **System-Logs**: Verbindungsprotokoll, Fehleraufzeichnungen
+
+### Vorteile gegenüber GUI-Tools
+- **Flexibilität**: Komplexe Abfragen jenseits vordefinierter Filter
+- **Performance**: Direkte Datenbankzugriffe ohne Interface-Overhead
+- **Automatisierung**: Skript-basierte Analysen für wiederkehrende Aufgaben
+- **Tiefe**: Zugriff auf Metadaten und versteckte Tabellenstrukturen
+
+## Grundlagen: SQLite-Struktur verstehen
+
+### Datenbank-Anatomie in der Forensik
+
+```sql
+-- Tabellen einer WhatsApp-Datenbank analysieren
+.tables
+
+-- Tabellenstruktur untersuchen
+.schema messages
+
+-- Beispiel-Output:
+CREATE TABLE messages (
+    _id INTEGER PRIMARY KEY AUTOINCREMENT,
+    key_remote_jid TEXT,
+    key_from_me INTEGER,
+    key_id TEXT,
+    status INTEGER,
+    needs_push INTEGER,
+    data TEXT,
+    timestamp INTEGER,
+    media_url TEXT,
+    media_mime_type TEXT,
+    media_wa_type INTEGER,
+    media_size INTEGER,
+    latitude REAL,
+    longitude REAL
+);
+```
+
+### SQLite-spezifische Forensik-Herausforderungen
+
+**WAL-Mode (Write-Ahead Logging)**:
+```sql
+-- WAL-Datei auf nicht-committete Transaktionen prüfen
+PRAGMA journal_mode;
+
+-- Temporäre Daten in WAL-Datei finden
+-- (Erfordert spezielle Tools wie sqlitewalreader)
+```
+
+**Gelöschte Records**:
+```sql
+-- Freespace-Analyse für gelöschte Daten
+-- Hinweis: Erfordert spezialisierte Recovery-Tools
+```
+
+## Timeline-Rekonstruktion: Der Forensik-Klassiker
+
+### Grundlegende Timeline-Abfrage
+
+```sql
+-- Chronologische Ereignisübersicht erstellen
+SELECT 
+    datetime(timestamp/1000, 'unixepoch', 'localtime') as ereignis_zeit,
+    CASE 
+        WHEN key_from_me = 1 THEN 'Ausgehend'
+        ELSE 'Eingehend'
+    END as richtung,
+    key_remote_jid as kontakt,
+    substr(data, 1, 50) || '...' as nachricht_preview
+FROM messages 
+WHERE timestamp > 0
+ORDER BY timestamp DESC
+LIMIT 100;
+```
+
+### Erweiterte Timeline mit Kontextinformationen
+
+```sql
+-- Timeline mit Geolocation und Media-Daten
+SELECT 
+    datetime(m.timestamp/1000, 'unixepoch', 'localtime') as zeitstempel,
+    c.display_name as kontakt_name,
+    CASE 
+        WHEN m.key_from_me = 1 THEN '→ Gesendet'
+        ELSE '← Empfangen'
+    END as richtung,
+    CASE 
+        WHEN m.media_wa_type IS NOT NULL THEN 'Media: ' || m.media_mime_type
+        ELSE 'Text'
+    END as nachricht_typ,
+    CASE 
+        WHEN m.latitude IS NOT NULL THEN 
+            'Standort: ' || ROUND(m.latitude, 6) || ', ' || ROUND(m.longitude, 6)
+        ELSE substr(m.data, 1, 100)
+    END as inhalt
+FROM messages m
+LEFT JOIN wa_contacts c ON m.key_remote_jid = c.jid
+WHERE m.timestamp BETWEEN 
+    strftime('%s', '2024-01-01') * 1000 AND 
+    strftime('%s', '2024-01-31') * 1000
+ORDER BY m.timestamp;
+```
+
+## Kommunikations-Analyse: Soziale Netzwerke aufdecken
+
+### Häufigste Kontakte identifizieren
+
+```sql
+-- Top-Kommunikationspartner nach Nachrichtenvolumen
+SELECT 
+    c.display_name,
+    m.key_remote_jid,
+    COUNT(*) as nachrichten_gesamt,
+    SUM(CASE WHEN m.key_from_me = 1 THEN 1 ELSE 0 END) as gesendet,
+    SUM(CASE WHEN m.key_from_me = 0 THEN 1 ELSE 0 END) as empfangen,
+    MIN(datetime(m.timestamp/1000, 'unixepoch', 'localtime')) as erster_kontakt,
+    MAX(datetime(m.timestamp/1000, 'unixepoch', 'localtime')) as letzter_kontakt
+FROM messages m
+LEFT JOIN wa_contacts c ON m.key_remote_jid = c.jid
+GROUP BY m.key_remote_jid
+HAVING nachrichten_gesamt > 10
+ORDER BY nachrichten_gesamt DESC;
+```
+
+### Kommunikationsmuster-Analyse
+
+```sql
+-- Tägliche Aktivitätsmuster
+SELECT 
+    strftime('%H', timestamp/1000, 'unixepoch', 'localtime') as stunde,
+    COUNT(*) as nachrichten_anzahl,
+    AVG(length(data)) as durchschnittliche_laenge
+FROM messages 
+WHERE timestamp > 0 AND data IS NOT NULL
+GROUP BY stunde
+ORDER BY stunde;
+```
+
+```sql
+-- Verdächtige Aktivitätsspitzen identifizieren
+WITH hourly_stats AS (
+    SELECT 
+        date(timestamp/1000, 'unixepoch', 'localtime') as tag,
+        strftime('%H', timestamp/1000, 'unixepoch', 'localtime') as stunde,
+        COUNT(*) as nachrichten_pro_stunde
+    FROM messages 
+    WHERE timestamp > 0
+    GROUP BY tag, stunde
+),
+avg_per_hour AS (
+    SELECT stunde, AVG(nachrichten_pro_stunde) as durchschnitt
+    FROM hourly_stats
+    GROUP BY stunde
+)
+SELECT 
+    h.tag,
+    h.stunde,
+    h.nachrichten_pro_stunde,
+    a.durchschnitt,
+    ROUND((h.nachrichten_pro_stunde - a.durchschnitt) / a.durchschnitt * 100, 2) as abweichung_prozent
+FROM hourly_stats h
+JOIN avg_per_hour a ON h.stunde = a.stunde
+WHERE h.nachrichten_pro_stunde > a.durchschnitt * 2
+ORDER BY abweichung_prozent DESC;
+```
+
+## Browser-Forensik: Digitale Spuren verfolgen
+
+### Chrome/Chromium History-Analyse
+
+```sql
+-- Browser-History mit Besuchshäufigkeit
+SELECT 
+    url,
+    title,
+    visit_count,
+    datetime(last_visit_time/1000000-11644473600, 'unixepoch', 'localtime') as letzter_besuch,
+    CASE 
+        WHEN typed_count > 0 THEN 'Direkt eingegeben'
+        ELSE 'Über Link/Verlauf'
+    END as zugriff_art
+FROM urls 
+WHERE last_visit_time > 0
+ORDER BY last_visit_time DESC
+LIMIT 100;
+```
+
+### Such-Verlauf analysieren
+
+```sql
+-- Google-Suchen aus Browser-History extrahieren
+SELECT 
+    datetime(last_visit_time/1000000-11644473600, 'unixepoch', 'localtime') as suchzeit,
+    CASE 
+        WHEN url LIKE '%google.com/search%' THEN 
+            replace(substr(url, instr(url, 'q=') + 2, 
+                   case when instr(substr(url, instr(url, 'q=') + 2), '&') > 0 
+                   then instr(substr(url, instr(url, 'q=') + 2), '&') - 1 
+                   else length(url) end), '+', ' ')
+        ELSE 'Andere Suchmaschine'
+    END as suchbegriff,
+    url
+FROM urls 
+WHERE url LIKE '%search%' OR url LIKE '%q=%'
+ORDER BY last_visit_time DESC;
+```
+
+## Anomalie-Erkennung mit SQL
+
+### Ungewöhnliche Datei-Zugriffe identifizieren
+
+```sql
+-- Dateizugriffe außerhalb der Arbeitszeiten
+WITH file_access AS (
+    SELECT 
+        datetime(timestamp, 'unixepoch', 'localtime') as zugriffszeit,
+        strftime('%H', timestamp, 'unixepoch', 'localtime') as stunde,
+        strftime('%w', timestamp, 'unixepoch', 'localtime') as wochentag,
+        file_path,
+        action_type
+    FROM file_access_logs
+)
+SELECT *
+FROM file_access
+WHERE (
+    stunde < '08' OR stunde > '18' OR  -- Außerhalb 8-18 Uhr
+    wochentag IN ('0', '6')           -- Wochenende
+) AND action_type IN ('read', 'write', 'delete')
+ORDER BY zugriffszeit DESC;
+```
+
+### Datenexfiltration-Indikatoren
+
+```sql
+-- Große Dateiübertragungen in kurzen Zeiträumen
+SELECT 
+    datetime(transfer_start, 'unixepoch', 'localtime') as start_zeit,
+    SUM(file_size) as gesamt_bytes,
+    COUNT(*) as anzahl_dateien,
+    destination_ip,
+    GROUP_CONCAT(DISTINCT file_extension) as dateitypen
+FROM network_transfers 
+WHERE transfer_start BETWEEN 
+    strftime('%s', 'now', '-7 days') AND strftime('%s', 'now')
+GROUP BY 
+    date(transfer_start, 'unixepoch', 'localtime'),
+    strftime('%H', transfer_start, 'unixepoch', 'localtime'),
+    destination_ip
+HAVING gesamt_bytes > 100000000  -- > 100MB
+ORDER BY gesamt_bytes DESC;
+```
+
+## Erweiterte Techniken: Window Functions und CTEs
+
+### Sliding Window-Analyse für Ereigniskorrelation
+
+```sql
+-- Ereignisse in 5-Minuten-Fenstern korrelieren
+WITH event_windows AS (
+    SELECT 
+        datetime(timestamp, 'unixepoch', 'localtime') as ereigniszeit,
+        event_type,
+        user_id,
+        LAG(timestamp, 1) OVER (PARTITION BY user_id ORDER BY timestamp) as prev_timestamp,
+        LEAD(timestamp, 1) OVER (PARTITION BY user_id ORDER BY timestamp) as next_timestamp
+    FROM security_events
+    ORDER BY timestamp
+)
+SELECT 
+    ereigniszeit,
+    event_type,
+    user_id,
+    CASE 
+        WHEN (timestamp - prev_timestamp) < 300 THEN 'Schnelle Aufeinanderfolge'
+        WHEN (next_timestamp - timestamp) < 300 THEN 'Vor schnellem Event'
+        ELSE 'Isoliert'
+    END as ereignis_kontext
+FROM event_windows;
+```
+
+### Temporäre Anomalie-Scores
+
+```sql
+-- Anomalie-Score basierend auf Abweichung vom Normalverhalten
+WITH user_baseline AS (
+    SELECT 
+        user_id,
+        AVG(daily_logins) as avg_logins,
+        STDEV(daily_logins) as stddev_logins
+    FROM (
+        SELECT 
+            user_id,
+            date(login_time, 'unixepoch', 'localtime') as login_date,
+            COUNT(*) as daily_logins
+        FROM user_logins
+        WHERE login_time > strftime('%s', 'now', '-30 days')
+        GROUP BY user_id, login_date
+    )
+    GROUP BY user_id
+    HAVING COUNT(*) > 7  -- Mindestens 7 Tage Daten
+),
+current_behavior AS (
+    SELECT 
+        user_id,
+        date(login_time, 'unixepoch', 'localtime') as login_date,
+        COUNT(*) as daily_logins
+    FROM user_logins
+    WHERE login_time > strftime('%s', 'now', '-7 days')
+    GROUP BY user_id, login_date
+)
+SELECT 
+    c.user_id,
+    c.login_date,
+    c.daily_logins,
+    b.avg_logins,
+    ROUND(ABS(c.daily_logins - b.avg_logins) / b.stddev_logins, 2) as anomalie_score
+FROM current_behavior c
+JOIN user_baseline b ON c.user_id = b.user_id
+WHERE anomalie_score > 2.0  -- Mehr als 2 Standardabweichungen
+ORDER BY anomalie_score DESC;
+```
+
+## Python-Integration für Automatisierung
+
+### SQLite-Forensik mit Python
+
+```python
+import sqlite3
+import pandas as pd
+from datetime import datetime
+import matplotlib.pyplot as plt
+
+class ForensicSQLAnalyzer:
+    def __init__(self, db_path):
+        self.conn = sqlite3.connect(db_path)
+        self.conn.row_factory = sqlite3.Row
+    
+    def extract_timeline(self, start_date=None, end_date=None):
+        """Timeline-Extraktion mit Datumsfilterung"""
+        query = """
+        SELECT 
+            datetime(timestamp/1000, 'unixepoch', 'localtime') as timestamp,
+            event_type,
+            details,
+            user_context
+        FROM events 
+        WHERE 1=1
+        """
+        
+        params = []
+        if start_date:
+            query += " AND timestamp >= ?"
+            params.append(int(start_date.timestamp() * 1000))
+        if end_date:
+            query += " AND timestamp <= ?"
+            params.append(int(end_date.timestamp() * 1000))
+            
+        query += " ORDER BY timestamp"
+        
+        return pd.read_sql_query(query, self.conn, params=params)
+    
+    def communication_analysis(self):
+        """Kommunikationsmuster analysieren"""
+        query = """
+        SELECT 
+            contact_id,
+            COUNT(*) as message_count,
+            AVG(message_length) as avg_length,
+            MIN(timestamp) as first_contact,
+            MAX(timestamp) as last_contact
+        FROM messages 
+        GROUP BY contact_id
+        HAVING message_count > 5
+        ORDER BY message_count DESC
+        """
+        
+        return pd.read_sql_query(query, self.conn)
+    
+    def detect_anomalies(self, threshold=2.0):
+        """Statistische Anomalie-Erkennung"""
+        query = """
+        WITH daily_stats AS (
+            SELECT 
+                date(timestamp, 'unixepoch', 'localtime') as day,
+                COUNT(*) as daily_events
+            FROM events
+            GROUP BY day
+        ),
+        stats AS (
+            SELECT 
+                AVG(daily_events) as mean_events,
+                STDEV(daily_events) as stddev_events
+            FROM daily_stats
+        )
+        SELECT 
+            d.day,
+            d.daily_events,
+            s.mean_events,
+            ABS(d.daily_events - s.mean_events) / s.stddev_events as z_score
+        FROM daily_stats d, stats s
+        WHERE z_score > ?
+        ORDER BY z_score DESC
+        """
+        
+        return pd.read_sql_query(query, self.conn, params=[threshold])
+    
+    def export_findings(self, filename):
+        """Ermittlungsergebnisse exportieren"""
+        timeline = self.extract_timeline()
+        comms = self.communication_analysis()
+        anomalies = self.detect_anomalies()
+        
+        with pd.ExcelWriter(filename) as writer:
+            timeline.to_excel(writer, sheet_name='Timeline', index=False)
+            comms.to_excel(writer, sheet_name='Communications', index=False)
+            anomalies.to_excel(writer, sheet_name='Anomalies', index=False)
+
+# Verwendung
+analyzer = ForensicSQLAnalyzer('/path/to/evidence.db')
+findings = analyzer.export_findings('investigation_findings.xlsx')
+```
+
+## Häufige Fallstricke und Best Practices
+
+### Datenintegrität sicherstellen
+
+```sql
+-- Konsistenz-Checks vor Analyse
+SELECT 
+    'Null Timestamps' as issue_type,
+    COUNT(*) as count
+FROM messages 
+WHERE timestamp IS NULL OR timestamp = 0
+
+UNION ALL
+
+SELECT 
+    'Missing Contact Info' as issue_type,
+    COUNT(*) as count
+FROM messages m
+LEFT JOIN wa_contacts c ON m.key_remote_jid = c.jid
+WHERE c.jid IS NULL;
+```
+
+### Performance-Optimierung
+
+```sql
+-- Index für häufige Abfragen erstellen
+CREATE INDEX IF NOT EXISTS idx_messages_timestamp 
+ON messages(timestamp);
+
+CREATE INDEX IF NOT EXISTS idx_messages_contact_timestamp 
+ON messages(key_remote_jid, timestamp);
+
+-- Query-Performance analysieren
+EXPLAIN QUERY PLAN 
+SELECT * FROM messages 
+WHERE timestamp BETWEEN ? AND ?
+ORDER BY timestamp;
+```
+
+### Forensische Dokumentation
+
+```sql
+-- Metadaten für Gerichtsverwertbarkeit dokumentieren
+SELECT 
+    'Database Schema Version' as info_type,
+    user_version as value
+FROM pragma_user_version
+
+UNION ALL
+
+SELECT 
+    'Last Modified',
+    datetime(mtime, 'unixepoch', 'localtime')
+FROM pragma_file_control;
+```
+
+## Spezialisierte Forensik-Szenarien
+
+### Mobile App-Forensik: Instagram-Datenbank
+
+```sql
+-- Instagram-Nachrichten mit Medien-Metadaten
+SELECT 
+    datetime(m.timestamp/1000, 'unixepoch', 'localtime') as nachricht_zeit,
+    u.username as absender,
+    CASE 
+        WHEN m.item_type = 1 THEN 'Text: ' || m.text
+        WHEN m.item_type = 2 THEN 'Bild: ' || mi.media_url
+        WHEN m.item_type = 3 THEN 'Video: ' || mi.media_url
+        ELSE 'Anderer Typ: ' || m.item_type
+    END as inhalt,
+    m.thread_key as chat_id
+FROM direct_messages m
+LEFT JOIN users u ON m.user_id = u.pk
+LEFT JOIN media_items mi ON m.media_id = mi.id
+WHERE m.timestamp > 0
+ORDER BY m.timestamp DESC;
+```
+
+### Incident Response: Systemprotokoll-Korrelation
+
+```sql
+-- Korrelation zwischen Login-Events und Netzwerk-Aktivität
+WITH suspicious_logins AS (
+    SELECT 
+        login_time,
+        user_id,
+        source_ip,
+        login_time + 3600 as investigation_window  -- 1 Stunde nach Login
+    FROM login_events 
+    WHERE source_ip NOT LIKE '192.168.%'  -- Externe IPs
+    AND login_time > strftime('%s', 'now', '-7 days')
+),
+network_activity AS (
+    SELECT 
+        connection_time,
+        source_ip,
+        destination_ip,
+        bytes_transferred,
+        protocol
+    FROM network_connections
+)
+SELECT 
+    datetime(sl.login_time, 'unixepoch', 'localtime') as verdaechtiger_login,
+    sl.user_id,
+    sl.source_ip as login_ip,
+    COUNT(na.connection_time) as netzwerk_aktivitaeten,
+    SUM(na.bytes_transferred) as gesamt_daten_bytes,
+    GROUP_CONCAT(DISTINCT na.destination_ip) as ziel_ips
+FROM suspicious_logins sl
+LEFT JOIN network_activity na ON 
+    na.connection_time BETWEEN sl.login_time AND sl.investigation_window
+    AND na.source_ip = sl.source_ip
+GROUP BY sl.login_time, sl.user_id, sl.source_ip
+HAVING netzwerk_aktivitaeten > 0
+ORDER BY gesamt_daten_bytes DESC;
+```
+
+## Erweiterte WAL-Analyse und Recovery
+
+### WAL-Datei Untersuchung
+
+```sql
+-- WAL-Mode Status prüfen
+PRAGMA journal_mode;
+PRAGMA wal_checkpoint;
+
+-- Uncommitted transactions in WAL identifizieren
+-- Hinweis: Erfordert spezielle Tools oder Hex-Editor
+-- Zeigt Konzept für manuelle Analyse
+
+SELECT 
+    name,
+    rootpage,
+    sql
+FROM sqlite_master 
+WHERE type = 'table'
+ORDER BY name;
+```
+
+### Gelöschte Daten-Recovery
+
+```python
+# Python-Script für erweiterte SQLite-Recovery
+import sqlite3
+import struct
+import os
+
+class SQLiteForensics:
+    def __init__(self, db_path):
+        self.db_path = db_path
+        self.page_size = self.get_page_size()
+    
+    def get_page_size(self):
+        """SQLite Page-Size ermitteln"""
+        with open(self.db_path, 'rb') as f:
+            f.seek(16)  # Page size offset
+            return struct.unpack('>H', f.read(2))[0]
+    
+    def analyze_freespace(self):
+        """Freespace auf gelöschte Records analysieren"""
+        conn = sqlite3.connect(self.db_path)
+        cursor = conn.cursor()
+        
+        # Freespace-Informationen sammeln
+        cursor.execute("PRAGMA freelist_count;")
+        free_pages = cursor.fetchone()[0]
+        
+        cursor.execute("PRAGMA page_count;")
+        total_pages = cursor.fetchone()[0]
+        
+        recovery_potential = {
+            'total_pages': total_pages,
+            'free_pages': free_pages,
+            'recovery_potential': f"{(free_pages/total_pages)*100:.2f}%"
+        }
+        
+        conn.close()
+        return recovery_potential
+    
+    def extract_unallocated(self):
+        """Unallocated Space für Recovery extrahieren"""
+        # Vereinfachtes Beispiel - echte Implementation erfordert
+        # detaillierte SQLite-Interna-Kenntnisse
+        unallocated_data = []
+        
+        with open(self.db_path, 'rb') as f:
+            file_size = os.path.getsize(self.db_path)
+            pages = file_size // self.page_size
+            
+            for page_num in range(1, pages + 1):
+                f.seek((page_num - 1) * self.page_size)
+                page_data = f.read(self.page_size)
+                
+                # Suche nach Text-Patterns in Freespace
+                # (Vereinfacht - echte Recovery ist komplexer)
+                if b'WhatsApp' in page_data or b'@' in page_data:
+                    unallocated_data.append({
+                        'page': page_num,
+                        'potential_data': page_data[:100]  # Erste 100 Bytes
+                    })
+        
+        return unallocated_data
+
+# Verwendung für Recovery-Assessment
+forensics = SQLiteForensics('/path/to/damaged.db')
+recovery_info = forensics.analyze_freespace()
+print(f"Recovery-Potenzial: {recovery_info['recovery_potential']}")
+```
+
+## Compliance und Rechtssicherheit
+
+### Audit-Trail erstellen
+
+```sql
+-- Forensische Dokumentation aller durchgeführten Abfragen
+CREATE TABLE IF NOT EXISTS forensic_audit_log (
+    id INTEGER PRIMARY KEY AUTOINCREMENT,
+    timestamp DATETIME DEFAULT CURRENT_TIMESTAMP,
+    investigator TEXT,
+    query_type TEXT,
+    sql_query TEXT,
+    affected_rows INTEGER,
+    case_number TEXT,
+    notes TEXT
+);
+
+-- Beispiel-Eintrag
+INSERT INTO forensic_audit_log 
+(investigator, query_type, sql_query, affected_rows, case_number, notes)
+VALUES 
+('Max Mustermann', 'TIMELINE_EXTRACTION', 
+ 'SELECT * FROM messages WHERE timestamp BETWEEN ? AND ?', 
+ 1247, 'CASE-2024-001', 
+ 'Timeline-Extraktion für Zeitraum 01.01.2024 - 31.01.2024');
+```
+
+### Hash-Verifikation implementieren
+
+```python
+import hashlib
+import sqlite3
+
+def verify_database_integrity(db_path, expected_hash=None):
+    """Datenbank-Integrität durch Hash-Verifikation prüfen"""
+    
+    # SHA-256 Hash der Datenbankdatei
+    sha256_hash = hashlib.sha256()
+    with open(db_path, "rb") as f:
+        for chunk in iter(lambda: f.read(4096), b""):
+            sha256_hash.update(chunk)
+    
+    current_hash = sha256_hash.hexdigest()
+    
+    # Zusätzlich: Struktureller Integritäts-Check
+    conn = sqlite3.connect(db_path)
+    cursor = conn.cursor()
+    
+    try:
+        cursor.execute("PRAGMA integrity_check;")
+        integrity_result = cursor.fetchall()
+        is_structurally_intact = integrity_result == [('ok',)]
+    except Exception as e:
+        is_structurally_intact = False
+        integrity_result = [f"Error: {str(e)}"]
+    finally:
+        conn.close()
+    
+    return {
+        'file_hash': current_hash,
+        'hash_matches': current_hash == expected_hash if expected_hash else None,
+        'structurally_intact': is_structurally_intact,
+        'integrity_details': integrity_result,
+        'verified_at': datetime.now().isoformat()
+    }
+
+# Chain of Custody dokumentieren
+def log_database_access(db_path, investigator, purpose):
+    """Datenbankzugriff für Chain of Custody protokollieren"""
+    verification = verify_database_integrity(db_path)
+    
+    log_entry = {
+        'timestamp': datetime.now().isoformat(),
+        'investigator': investigator,
+        'database_path': db_path,
+        'access_purpose': purpose,
+        'pre_access_hash': verification['file_hash'],
+        'database_integrity': verification['structurally_intact']
+    }
+    
+    # Log in separater Audit-Datei speichern
+    with open('forensic_access_log.json', 'a') as log_file:
+        json.dump(log_entry, log_file)
+        log_file.write('\n')
+    
+    return log_entry
+```
+
+## Fazit und Weiterführende Ressourcen
+
+SQL in der digitalen Forensik ist mehr als nur Datenbankabfragen - es ist ein mächtiges Werkzeug für:
+
+- **Timeline-Rekonstruktion** mit präziser zeitlicher Korrelation
+- **Kommunikationsanalyse** für soziale Netzwerk-Aufklärung  
+- **Anomalie-Erkennung** durch statistische Analyse
+- **Automatisierung** wiederkehrender Untersuchungsschritte
+- **Tiefe Datenextraktion** jenseits GUI-Limitationen
+
+### Nächste Schritte
+
+1. **Praktische Übung**: Beginnen Sie mit einfachen WhatsApp-Datenbank-Analysen
+2. **Tool-Integration**: Kombinieren Sie SQL mit Python für erweiterte Analysen
+3. **Spezialisierung**: Vertiefen Sie mobile-spezifische oder Browser-Forensik
+4. **Automation**: Entwickeln Sie wiederverwendbare SQL-Scripts für häufige Szenarien
+5. **Rechtssicherheit**: Implementieren Sie Audit-Trails und Hash-Verifikation
+
+### Empfohlene Tools
+
+- **DB Browser for SQLite**: GUI für interaktive Exploration
+- **SQLiteStudio**: Erweiterte SQLite-Verwaltung
+- **Python sqlite3**: Programmbasierte Automatisierung
+- **Autopsy**: Integration in forensische Workflows
+- **Cellebrite UFED**: Mobile Forensik mit SQL-Export
+
+Die Kombination aus SQL-Kenntnissen und forensischem Verständnis macht moderne Ermittler zu hocheffizienten Datenanalytikern. In einer Welt zunehmender Datenmengen wird diese Fähigkeit zum entscheidenden Wettbewerbsvorteil.
\ No newline at end of file
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+---
+title: "Timeline-Analyse & Event-Korrelation: Methodische Rekonstruktion forensischer Ereignisse"
+description: "Umfassende Anleitung zur systematischen Timeline-Erstellung aus heterogenen Datenquellen, Super-Timeline-Processing und Advanced-Correlation-Techniken für komplexe Incident-Response-Szenarien."
+author: "Claude 4 Sonnett (Prompt: Mario Stöckl)"
+last_updated: 2025-08-10
+difficulty: advanced
+categories: ["analysis", "methodology", "incident-response"]
+tags: ["timeline-correlation", "event-sequencing", "temporal-analysis", "super-timeline", "pivot-points", "behavioral-patterns", "anomaly-detection", "anti-forensics-detection", "incident-response", "log2timeline", "plaso"]
+tool_name: "Timeline Analysis & Event Correlation"
+related_tools: ["Autopsy", "Volatility", "Wireshark", "SIFT Workstation"]
+published: true
+---
+
+# Timeline-Analyse & Event-Korrelation: Methodische Rekonstruktion forensischer Ereignisse
+
+Timeline-Analyse bildet das Rückgrat moderner forensischer Untersuchungen und ermöglicht die chronologische Rekonstruktion von Ereignissen aus heterogenen digitalen Artefakten. Diese methodische Herangehensweise korreliert zeitbasierte Evidenz für präzise Incident-Response und belastbare Beweisführung.
+
+## Grundlagen der forensischen Timeline-Analyse
+
+### Was ist Timeline-Analyse?
+
+Timeline-Analyse ist die systematische Korrelation zeitbasierter Artefakte aus verschiedenen digitalen Quellen zur Rekonstruktion von Ereignissequenzen. Sie ermöglicht Forensikern, das "Was", "Wann", "Wo" und "Wie" von Sicherheitsvorfällen zu verstehen.
+
+**Kernprinzipien:**
+- **Chronologische Ordnung**: Alle Ereignisse werden in temporaler Reihenfolge arrangiert
+- **Multi-Source-Integration**: Daten aus verschiedenen Systemen werden vereint
+- **Zeitstempel-Normalisierung**: UTC-Konvertierung für einheitliche Referenz
+- **Korrelationsbasierte Analyse**: Zusammenhänge zwischen scheinbar unabhängigen Events
+
+### Typologie forensischer Zeitstempel
+
+**MAC-Times (Modified, Accessed, Created)**
+```
+Filesystem-Timestamps:
+- $STANDARD_INFORMATION (SI) - NTFS-Metadaten
+- $FILE_NAME (FN) - Directory-Entry-Timestamps  
+- Born Date - Erste Erstellung im Filesystem
+- $USNJrnl - Change Journal Entries
+```
+
+**Registry-Timestamps**
+```
+Windows Registry:
+- Key Last Write Time - Letzte Modifikation
+- Value Creation Time - Wert-Erstellung
+- Hive Load Time - Registry-Hive-Mounting
+```
+
+**Event-Log-Timestamps**
+```
+Windows Event Logs:
+- TimeCreated - Event-Generierung
+- TimeWritten - Log-Persistierung  
+- CorrelationActivityID - Cross-System-Tracking
+```
+
+## Super-Timeline-Erstellung: Methodisches Vorgehen
+
+### Phase 1: Artefakt-Akquisition und Preprocessing
+
+**Datenquellen-Inventar erstellen:**
+
+```bash
+# Filesystem-Timeline mit fls
+fls -r -p -m /mnt/evidence/image.dd > filesystem_timeline.body
+
+# Registry-Timeline mit regtime
+regtime.py -r /mnt/evidence/registry/ > registry_timeline.csv
+
+# Event-Log-Extraktion mit python-evtx
+evtx_dump.py Security.evtx > security_events.xml
+```
+
+**Memory-Artefakte integrieren:**
+```bash
+# Volatility Timeline-Generierung
+vol.py -f memory.vmem --profile=Win10x64 timeliner > memory_timeline.csv
+
+# Process-Timeline mit detailed Metadata
+vol.py -f memory.vmem --profile=Win10x64 pslist -v > process_details.txt
+```
+
+### Phase 2: Zeitstempel-Normalisierung und UTC-Konvertierung
+
+**Timezone-Handling:**
+```python
+# Python-Script für Timezone-Normalisierung
+import datetime
+import pytz
+
+def normalize_timestamp(timestamp_str, source_timezone):
+    """
+    Konvertiert lokale Timestamps zu UTC für einheitliche Timeline
+    """
+    local_tz = pytz.timezone(source_timezone)
+    dt = datetime.datetime.strptime(timestamp_str, '%Y-%m-%d %H:%M:%S')
+    localized_dt = local_tz.localize(dt)
+    utc_dt = localized_dt.astimezone(pytz.utc)
+    return utc_dt.strftime('%Y-%m-%d %H:%M:%S UTC')
+```
+
+**Anti-Timestomp-Detection:**
+```bash
+# Timestomp-Anomalien identifizieren
+analyzeMFT.py -f $MFT -o mft_analysis.csv
+# Suche nach: SI-Time < FN-Time (Timestomp-Indikator)
+```
+
+### Phase 3: Log2timeline/PLASO Super-Timeline-Processing
+
+**PLASO-basierte Timeline-Generierung:**
+```bash
+# Multi-Source-Timeline mit log2timeline
+log2timeline.py --storage-file evidence.plaso \
+    --parsers "win7,chrome,firefox,skype" \
+    --timezone "Europe/Berlin" \
+    /mnt/evidence/
+
+# CSV-Export für Analysis
+psort.py -w timeline_super.csv evidence.plaso
+```
+
+**Advanced PLASO-Filtering:**
+```bash
+# Zeitfenster-spezifische Extraktion
+psort.py -w incident_window.csv \
+    --date-filter "2024-01-10,2024-01-12" \
+    evidence.plaso
+
+# Ereignis-spezifisches Filtering
+psort.py -w web_activity.csv \
+    --filter "parser contains 'chrome'" \
+    evidence.plaso
+```
+
+## Advanced Correlation-Techniken
+
+### Pivot-Point-Identifikation
+
+**Initial Compromise Detection:**
+```sql
+-- SQL-basierte Timeline-Analyse (bei CSV-Import in DB)
+SELECT timestamp, source, event_type, description
+FROM timeline 
+WHERE description LIKE '%powershell%' 
+   OR description LIKE '%cmd.exe%'
+   OR description LIKE '%rundll32%'
+ORDER BY timestamp;
+```
+
+**Lateral Movement Patterns:**
+```python
+# Python-Script für Lateral-Movement-Detection
+def detect_lateral_movement(timeline_data):
+    """
+    Identifiziert suspicious Login-Patterns über Zeitfenster
+    """
+    login_events = timeline_data[
+        timeline_data['event_type'].str.contains('4624|4625', na=False)
+    ]
+    
+    # Gruppierung nach Source-IP und Zeitfenster-Analyse
+    suspicious_logins = login_events.groupby(['source_ip']).apply(
+        lambda x: len(x[x['timestamp'].diff().dt.seconds < 300]) > 5
+    )
+    
+    return suspicious_logins[suspicious_logins == True]
+```
+
+### Behavioral Pattern Recognition
+
+**User Activity Profiling:**
+```bash
+# Regelmäßige Aktivitätsmuster extrahieren
+grep -E "(explorer\.exe|chrome\.exe|outlook\.exe)" timeline.csv | \
+awk -F',' '{print substr($1,1,10), $3}' | \
+sort | uniq -c | sort -nr
+```
+
+**Anomalie-Detection durch Statistical Analysis:**
+```python
+import pandas as pd
+from scipy import stats
+
+def detect_activity_anomalies(timeline_df):
+    """
+    Identifiziert ungewöhnliche Aktivitätsmuster via Z-Score
+    """
+    # Aktivität pro Stunde aggregieren
+    timeline_df['hour'] = pd.to_datetime(timeline_df['timestamp']).dt.hour
+    hourly_activity = timeline_df.groupby('hour').size()
+    
+    # Z-Score Berechnung für Anomalie-Detection
+    z_scores = stats.zscore(hourly_activity)
+    anomalous_hours = hourly_activity[abs(z_scores) > 2]
+    
+    return anomalous_hours
+```
+
+## Network-Event-Korrelation
+
+### Cross-System Timeline Correlation
+
+**SIEM-Integration für Multi-Host-Korrelation:**
+```bash
+# Splunk-Query für korrelierte Events
+index=windows EventCode=4624 OR EventCode=4625 OR EventCode=4648
+| eval login_time=strftime(_time, "%Y-%m-%d %H:%M:%S")
+| stats values(EventCode) as event_codes by src_ip, login_time
+| where mvcount(event_codes) > 1
+```
+
+**Network Flow Timeline Integration:**
+```python
+# Zeek/Bro-Logs mit Filesystem-Timeline korrelieren
+def correlate_network_filesystem(conn_logs, file_timeline):
+    """
+    Korreliert Netzwerk-Connections mit File-Access-Patterns
+    """
+    # Zeitfenster-basierte Korrelation (±30 Sekunden)
+    correlations = []
+    
+    for _, conn in conn_logs.iterrows():
+        conn_time = pd.to_datetime(conn['ts'])
+        time_window = pd.Timedelta(seconds=30)
+        
+        related_files = file_timeline[
+            (pd.to_datetime(file_timeline['timestamp']) >= conn_time - time_window) &
+            (pd.to_datetime(file_timeline['timestamp']) <= conn_time + time_window)
+        ]
+        
+        if not related_files.empty:
+            correlations.append({
+                'connection': conn,
+                'related_files': related_files,
+                'correlation_strength': len(related_files)
+            })
+    
+    return correlations
+```
+
+## Anti-Forensik-Detection durch Timeline-Inkonsistenzen
+
+### Timestamp Manipulation Detection
+
+**Timestomp-Pattern-Analyse:**
+```bash
+# MFT-Analyse für Timestomp-Detection
+analyzeMFT.py -f \$MFT -o mft_full.csv
+
+# Suspekte Timestamp-Patterns identifizieren
+python3 << EOF
+import pandas as pd
+import numpy as np
+
+mft_data = pd.read_csv('mft_full.csv')
+
+# Pattern 1: SI-Time vor FN-Time (klassischer Timestomp)
+timestomp_candidates = mft_data[
+    pd.to_datetime(mft_data['SI_Modified']) < pd.to_datetime(mft_data['FN_Modified'])
+]
+
+# Pattern 2: Unrealistische Timestamps (z.B. 1980-01-01)
+epoch_anomalies = mft_data[
+    pd.to_datetime(mft_data['SI_Created']).dt.year < 1990
+]
+
+print(f"Potential Timestomp: {len(timestomp_candidates)} files")
+print(f"Epoch Anomalies: {len(epoch_anomalies)} files")
+EOF
+```
+
+### Event Log Manipulation Detection
+
+**Windows Event Log Gap Analysis:**
+```python
+def detect_log_gaps(event_log_df):
+    """
+    Identifiziert verdächtige Lücken in Event-Log-Sequenzen
+    """
+    # Event-Record-IDs sollten sequenziell sein
+    event_log_df['RecordNumber'] = pd.to_numeric(event_log_df['RecordNumber'])
+    event_log_df = event_log_df.sort_values('RecordNumber')
+    
+    # Gaps in Record-Sequenz finden
+    record_diffs = event_log_df['RecordNumber'].diff()
+    large_gaps = record_diffs[record_diffs > 100]  # Threshold anpassbar
+    
+    return large_gaps
+```
+
+## Automated Timeline Processing & ML-basierte Anomalie-Erkennung
+
+### Machine Learning für Pattern Recognition
+
+**Unsupervised Clustering für Event-Gruppierung:**
+```python
+from sklearn.cluster import DBSCAN
+from sklearn.feature_extraction.text import TfidfVectorizer
+import pandas as pd
+
+def cluster_timeline_events(timeline_df):
+    """
+    Gruppiert ähnliche Events via DBSCAN-Clustering
+    """
+    # TF-IDF für Event-Descriptions
+    vectorizer = TfidfVectorizer(max_features=1000, stop_words='english')
+    event_vectors = vectorizer.fit_transform(timeline_df['description'])
+    
+    # DBSCAN-Clustering
+    clustering = DBSCAN(eps=0.5, min_samples=5).fit(event_vectors.toarray())
+    timeline_df['cluster'] = clustering.labels_
+    
+    # Anomalie-Events (Cluster -1)
+    anomalous_events = timeline_df[timeline_df['cluster'] == -1]
+    
+    return timeline_df, anomalous_events
+```
+
+**Time-Series-Anomalie-Detection:**
+```python
+from sklearn.ensemble import IsolationForest
+import matplotlib.pyplot as plt
+
+def detect_temporal_anomalies(timeline_df):
+    """
+    Isolation Forest für zeitbasierte Anomalie-Detection
+    """
+    # Stündliche Aktivität aggregieren
+    timeline_df['timestamp'] = pd.to_datetime(timeline_df['timestamp'])
+    hourly_activity = timeline_df.groupby(
+        timeline_df['timestamp'].dt.floor('H')
+    ).size().reset_index(name='event_count')
+    
+    # Isolation Forest Training
+    iso_forest = IsolationForest(contamination=0.1)
+    anomaly_labels = iso_forest.fit_predict(
+        hourly_activity[['event_count']]
+    )
+    
+    # Anomale Zeitfenster identifizieren
+    hourly_activity['anomaly'] = anomaly_labels
+    anomalous_periods = hourly_activity[hourly_activity['anomaly'] == -1]
+    
+    return anomalous_periods
+```
+
+## Enterprise-Scale Timeline Processing
+
+### Distributed Processing für große Datasets
+
+**Apache Spark für Big-Data-Timeline-Analyse:**
+```python
+from pyspark.sql import SparkSession
+from pyspark.sql.functions import *
+
+def process_enterprise_timeline(spark_session, timeline_path):
+    """
+    Spark-basierte Verarbeitung für TB-große Timeline-Daten
+    """
+    # Timeline-Daten laden
+    timeline_df = spark_session.read.csv(
+        timeline_path, 
+        header=True, 
+        inferSchema=True
+    )
+    
+    # Zeitfenster-basierte Aggregation
+    windowed_activity = timeline_df \
+        .withColumn("timestamp", to_timestamp("timestamp")) \
+        .withColumn("hour_window", window("timestamp", "1 hour")) \
+        .groupBy("hour_window", "source_system") \
+        .agg(
+            count("*").alias("event_count"),
+            countDistinct("user").alias("unique_users"),
+            collect_set("event_type").alias("event_types")
+        )
+    
+    return windowed_activity
+```
+
+### Cloud-Forensics Timeline Integration
+
+**AWS CloudTrail Timeline Correlation:**
+```bash
+# CloudTrail-Events mit lokaler Timeline korrelieren
+aws logs filter-log-events \
+    --log-group-name CloudTrail \
+    --start-time 1642636800000 \
+    --end-time 1642723200000 \
+    --filter-pattern "{ $.eventName = \"AssumeRole\" }" \
+    --output json > cloudtrail_events.json
+
+# JSON zu CSV für Timeline-Integration
+jq -r '.events[] | [.eventTime, .sourceIPAddress, .eventName, .userIdentity.type] | @csv' \
+    cloudtrail_events.json > cloudtrail_timeline.csv
+```
+
+## Praktische Anwendungsszenarien
+
+### Szenario 1: Advanced Persistent Threat (APT) Investigation
+
+**Mehrstufige Timeline-Analyse:**
+
+1. **Initial Compromise Detection:**
+```bash
+# Web-Browser-Downloads mit Malware-Signaturen korrelieren
+grep -E "(\.exe|\.zip|\.pdf)" browser_downloads.csv | \
+while read line; do
+    timestamp=$(echo $line | cut -d',' -f1)
+    filename=$(echo $line | cut -d',' -f3)
+    
+    # Hash-Verification gegen IOC-Liste
+    sha256=$(sha256sum "/mnt/evidence/$filename" 2>/dev/null | cut -d' ' -f1)
+    grep -q "$sha256" ioc_hashes.txt && echo "IOC Match: $timestamp - $filename"
+done
+```
+
+2. **Lateral Movement Tracking:**
+```sql
+-- Cross-System-Bewegung via RDP/SMB
+SELECT t1.timestamp, t1.source_ip, t2.timestamp, t2.dest_ip
+FROM network_timeline t1
+JOIN filesystem_timeline t2 ON 
+    t2.timestamp BETWEEN t1.timestamp AND t1.timestamp + INTERVAL 5 MINUTE
+WHERE t1.protocol = 'RDP' AND t2.activity_type = 'file_creation'
+ORDER BY t1.timestamp;
+```
+
+### Szenario 2: Insider-Threat-Analyse
+
+**Behavioral Baseline vs. Anomalie-Detection:**
+```python
+def analyze_insider_threat(user_timeline, baseline_days=30):
+    """
+    Vergleicht User-Aktivität mit historischer Baseline
+    """
+    # Baseline-Zeitraum definieren
+    baseline_end = pd.to_datetime('2024-01-01')
+    baseline_start = baseline_end - pd.Timedelta(days=baseline_days)
+    
+    baseline_activity = user_timeline[
+        (user_timeline['timestamp'] >= baseline_start) &
+        (user_timeline['timestamp'] <= baseline_end)
+    ]
+    
+    # Anomale Aktivitätsmuster
+    analysis_period = user_timeline[
+        user_timeline['timestamp'] > baseline_end
+    ]
+    
+    # Metriken: Off-Hours-Activity, Data-Volume, Access-Patterns
+    baseline_metrics = calculate_user_metrics(baseline_activity)
+    current_metrics = calculate_user_metrics(analysis_period)
+    
+    anomaly_score = compare_metrics(baseline_metrics, current_metrics)
+    
+    return anomaly_score
+```
+
+## Herausforderungen und Lösungsansätze
+
+### Challenge 1: Timezone-Komplexität in Multi-Domain-Umgebungen
+
+**Problem:** Inkonsistente Timezones zwischen Systemen führen zu falschen Korrelationen.
+
+**Lösung:**
+```python
+def unified_timezone_conversion(timeline_entries):
+    """
+    Intelligente Timezone-Detection und UTC-Normalisierung
+    """
+    timezone_mapping = {
+        'windows_local': 'Europe/Berlin',
+        'unix_utc': 'UTC',
+        'web_browser': 'client_timezone'  # Aus Browser-Metadaten
+    }
+    
+    for entry in timeline_entries:
+        source_tz = detect_timezone_from_source(entry['source'])
+        entry['timestamp_utc'] = convert_to_utc(
+            entry['timestamp'], 
+            timezone_mapping.get(source_tz, 'UTC')
+        )
+    
+    return timeline_entries
+```
+
+### Challenge 2: Volume-Skalierung bei Enterprise-Investigations
+
+**Problem:** TB-große Timeline-Daten überschreiten Memory-Kapazitäten.
+
+**Lösung - Streaming-basierte Verarbeitung:**
+```python
+def stream_process_timeline(file_path, chunk_size=10000):
+    """
+    Memory-effiziente Timeline-Processing via Chunks
+    """
+    for chunk in pd.read_csv(file_path, chunksize=chunk_size):
+        # Chunk-weise Verarbeitung
+        processed_chunk = apply_timeline_analysis(chunk)
+        
+        # Streaming-Output zu aggregated Results
+        yield processed_chunk
+```
+
+### Challenge 3: Anti-Forensik und Timeline-Manipulation
+
+**Problem:** Adversaries manipulieren Timestamps zur Evidence-Destruction.
+
+**Lösung - Multi-Source-Validation:**
+```bash
+# Cross-Reference-Validation zwischen verschiedenen Timestamp-Quellen
+python3 << EOF
+# $MFT vs. $UsnJrnl vs. Event-Logs vs. Registry
+def validate_timestamp_integrity(file_path):
+    sources = {
+        'mft_si': get_mft_si_time(file_path),
+        'mft_fn': get_mft_fn_time(file_path), 
+        'usnjrnl': get_usnjrnl_time(file_path),
+        'prefetch': get_prefetch_time(file_path),
+        'eventlog': get_eventlog_time(file_path)
+    }
+    
+    # Timestamp-Inkonsistenzen identifizieren
+    inconsistencies = detect_timestamp_discrepancies(sources)
+    confidence_score = calculate_integrity_confidence(sources)
+    
+    return inconsistencies, confidence_score
+EOF
+```
+
+## Tool-Integration und Workflow-Optimierung
+
+### Timeline-Tool-Ecosystem
+
+**Core-Tools-Integration:**
+```bash
+#!/bin/bash
+# Comprehensive Timeline-Workflow-Automation
+
+# 1. Multi-Source-Acquisition
+log2timeline.py --storage-file case.plaso \
+    --parsers "win7,chrome,firefox,apache,nginx" \
+    --hashers "sha256" \
+    /mnt/evidence/
+
+# 2. Memory-Timeline-Integration  
+volatility -f memory.vmem --profile=Win10x64 timeliner \
+    --output=csv --output-file=memory_timeline.csv
+
+# 3. Network-Timeline-Addition
+zeek -r network.pcap Log::default_path=/tmp/zeek_logs/
+python3 zeek_to_timeline.py /tmp/zeek_logs/ > network_timeline.csv
+
+# 4. Timeline-Merge und Analysis
+psort.py -w comprehensive_timeline.csv case.plaso
+python3 merge_timelines.py comprehensive_timeline.csv \
+    memory_timeline.csv network_timeline.csv > unified_timeline.csv
+
+# 5. Advanced-Analysis-Pipeline
+python3 timeline_analyzer.py unified_timeline.csv \
+    --detect-anomalies --pivot-analysis --correlation-strength=0.7
+```
+
+### Autopsy Timeline-Viewer Integration
+
+**Autopsy-Import für Visual Timeline Analysis:**
+```python
+def export_autopsy_timeline(timeline_df, case_name):
+    """
+    Konvertiert Timeline zu Autopsy-kompatiblem Format
+    """
+    autopsy_format = timeline_df[['timestamp', 'source', 'event_type', 'description']].copy()
+    autopsy_format['timestamp'] = pd.to_datetime(autopsy_format['timestamp']).astype(int) // 10**9
+    
+    # Autopsy-CSV-Format
+    autopsy_format.to_csv(f"{case_name}_autopsy_timeline.csv", 
+                         columns=['timestamp', 'source', 'event_type', 'description'],
+                         index=False)
+```
+
+## Fazit und Best Practices
+
+Timeline-Analyse repräsentiert eine fundamentale Investigationstechnik, die bei korrekter Anwendung präzise Incident-Rekonstruktion ermöglicht. Die Kombination aus methodischer Multi-Source-Integration, Advanced-Correlation-Techniken und ML-basierter Anomalie-Detection bildet die Basis für moderne forensische Untersuchungen.
+
+**Key Success Factors:**
+
+1. **Systematic Approach**: Strukturierte Herangehensweise von Akquisition bis Analysis
+2. **Multi-Source-Validation**: Cross-Reference zwischen verschiedenen Artefakt-Typen  
+3. **Timezone-Awareness**: Konsistente UTC-Normalisierung für akkurate Korrelation
+4. **Anti-Forensik-Resistenz**: Detection von Timestamp-Manipulation und Evidence-Destruction
+5. **Scalability-Design**: Enterprise-fähige Processing-Pipelines für Big-Data-Szenarien
+
+Die kontinuierliche Weiterentwicklung von Adversary-Techniken erfordert adaptive Timeline-Methoden, die sowohl traditionelle Artefakte als auch moderne Cloud- und Container-Umgebungen erfassen. Die Integration von Machine Learning in Timeline-Workflows eröffnet neue Möglichkeiten für automatisierte Anomalie-Detection und Pattern-Recognition bei gleichzeitiger Reduktion des manuellen Aufwands.
+
+**Nächste Schritte:**
+- Vertiefung spezifischer Tool-Implementierungen (Autopsy, SIFT, etc.)
+- Cloud-native Timeline-Techniken für AWS/Azure-Umgebungen
+- Advanced Correlation-Algorithmen für Zero-Day-Detection
+- Integration von Threat-Intelligence in Timeline-Workflows
\ No newline at end of file
diff --git a/src/content/knowledgebase/android-logical-imaging.md b/src/content/knowledgebase/method-android-logical-imaging.md
similarity index 99%
rename from src/content/knowledgebase/android-logical-imaging.md
rename to src/content/knowledgebase/method-android-logical-imaging.md
index 93628a5..96d6cbf 100644
--- a/src/content/knowledgebase/android-logical-imaging.md
+++ b/src/content/knowledgebase/method-android-logical-imaging.md
@@ -3,7 +3,7 @@ title: "Extraktion logischer Dateisysteme alter Android-Smartphones - eine KI-Re
 tool_name: "Android Logical Imaging"
 description: "Wie man alte Android-Handys aufbekommen könnte - eine Recherche von Claude"
 last_updated: 2025-07-21
-author: "Claude 4 Sonnet (Research)"
+author: "Claude 4 Sonnett (Prompt: Mario Stöckl)"
 difficulty: "advanced"
 categories: ["data-collection"]
 tags: ["imaging", "filesystem", "hardware-interface"]
diff --git a/src/content/knowledgebase/regular-expressions-regex.md b/src/content/knowledgebase/regular-expressions-regex.md
deleted file mode 100644
index cb9aa72..0000000
--- a/src/content/knowledgebase/regular-expressions-regex.md
+++ /dev/null
@@ -1,110 +0,0 @@
----
-title: "Regular Expressions (Regex) – Musterbasierte Textanalyse"
-tool_name: "Regular Expressions (Regex)"
-description: "Pattern matching language für Suche, Extraktion und Manipulation von Text in forensischen Analysen."
-last_updated: 2025-07-20
-author: "Claude 4 Sonnet"
-difficulty: "intermediate"
-categories: ["incident-response", "malware-analysis", "network-forensics", "fraud-investigation"]
-tags: ["pattern-matching", "text-processing", "log-analysis", "string-manipulation", "search-algorithms"]
-sections:
-  overview: true
-  installation: false
-  configuration: false
-  usage_examples: true
-  best_practices: true
-  troubleshooting: false
-  advanced_topics: true
-review_status: "published"
----
-
-> **⚠️ Hinweis**: Dies ist ein vorläufiger, KI-generierter Knowledgebase-Eintrag. Wir freuen uns über Verbesserungen und Ergänzungen durch die Community!
-
-
-# Übersicht
-
-**Regular Expressions (Regex)** sind ein leistungsfähiges Werkzeug zur Erkennung, Extraktion und Transformation von Zeichenfolgen anhand vordefinierter Muster. In der digitalen Forensik sind Regex-Ausdrücke unverzichtbar: Sie helfen beim Auffinden von IP-Adressen, Hash-Werten, Dateipfaden, Malware-Signaturen oder Kreditkartennummern in großen Mengen unstrukturierter Daten wie Logdateien, Netzwerktraces oder Memory Dumps.
-
-Regex ist nicht auf eine bestimmte Plattform oder Software beschränkt – es wird in nahezu allen gängigen Programmiersprachen, Texteditoren und forensischen Tools unterstützt.
-
-## Verwendungsbeispiele
-
-### 1. IP-Adressen extrahieren
-
-```regex
-\b(?:\d{1,3}\.){3}\d{1,3}\b
-````
-
-Verwendung:
-
-* Finden von IP-Adressen in Firewall-Logs oder Packet Captures.
-* Beispiel-Zeile:
-
-  ```
-  Connection from 192.168.1.101 to port 443 established
-  ```
-
-### 2. E-Mail-Adressen identifizieren
-
-```regex
-[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}
-```
-
-Verwendung:
-
-* Erkennung von kompromittierten Accounts in Phishing-E-Mails.
-* Analyse von Useraktivitäten oder Kommunikationsverläufen.
-
-### 3. Hash-Werte erkennen (z. B. SHA-256)
-
-```regex
-\b[A-Fa-f0-9]{64}\b
-```
-
-Verwendung:
-
-* Extraktion von Malware-Hashes aus Memory Dumps oder YARA-Logs.
-
-### 4. Zeitstempel in Logdateien extrahieren
-
-```regex
-\d{4}-\d{2}-\d{2}[ T]\d{2}:\d{2}:\d{2}
-```
-
-Verwendung:
-
-* Zeitsensitive Korrelationsanalysen (z. B. bei Intrusion Detection oder Timeline-Rekonstruktionen).
-
-## Best Practices
-
-* **Regex testen**: Nutze Plattformen wie [regexr.com](https://regexr.com/) oder [regex101.com](https://regex101.com/) zur Validierung.
-* **Performance beachten**: Komplexe Ausdrücke können ineffizient sein und Systeme verlangsamen – verwende Lazy Quantifiers (`*?`, `+?`) bei Bedarf.
-* **Escape-Zeichen korrekt anwenden**: Spezielle Zeichen wie `.` oder `\` müssen bei Bedarf mit `\\` oder `\.` maskiert werden.
-* **Portabilität prüfen**: Unterschiedliche Regex-Engines (z. B. Python `re`, PCRE, JavaScript) interpretieren manche Syntax leicht unterschiedlich.
-* **Lesbarkeit fördern**: Verwende benannte Gruppen (`(?P<name>...)`) und Kommentare (`(?x)`), um reguläre Ausdrücke besser wartbar zu machen.
-
-## Weiterführende Themen
-
-### Lookaheads und Lookbehinds
-
-Mit **Lookaheads** (`(?=...)`) und **Lookbehinds** (`(?<=...)`) können Bedingungen formuliert werden, ohne dass der Text Teil des Matchs wird.
-
-Beispiel: Alle `.exe`-Dateinamen **ohne** das Wort `safe` davor matchen:
-
-```regex
-(?<!safe\s)[\w-]+\.exe
-```
-
-### Regex in Forensik-Tools
-
-* **YARA**: Unterstützt Regex zur Erstellung von Malware-Signaturen.
-* **Wireshark**: Filtert Payloads anhand von Regex-ähnlicher Syntax.
-* **Splunk & ELK**: Verwenden Regex für Logparsing und Visualisierung.
-* **Volatility Plugins**: Extrahieren Artefakte mit Regex-basierten Scans.
-
----
-
-> 🔤 **Regex ist ein universelles Werkzeug für Analysten, Ermittler und Entwickler, um versteckte Informationen schnell und flexibel aufzuspüren.**
->
-> Nutze es überall dort, wo Textdaten eine Rolle spielen.
-
diff --git a/src/content/knowledgebase/kali-linux.md b/src/content/knowledgebase/tool-kali-linux.md
similarity index 98%
rename from src/content/knowledgebase/kali-linux.md
rename to src/content/knowledgebase/tool-kali-linux.md
index 1b52577..4c54cd8 100644
--- a/src/content/knowledgebase/kali-linux.md
+++ b/src/content/knowledgebase/tool-kali-linux.md
@@ -2,8 +2,8 @@
 title: "Kali Linux - Die Hacker-Distribution für Forensik & Penetration Testing"
 tool_name: "Kali Linux"
 description: "Leitfaden zur Installation, Nutzung und Best Practices für Kali Linux – die All-in-One-Plattform für Security-Profis."
-last_updated: 2025-07-20
-author: "Claude 4 Sonnet"
+last_updated: 2025-08-10
+author: "Claude 4 Sonnett (Prompt: Mario Stöckl)"
 difficulty: "intermediate"
 categories: ["incident-response", "forensics", "penetration-testing"]
 tags: ["live-boot", "tool-collection", "penetration-testing", "forensics-suite", "virtualization", "arm-support"]
diff --git a/src/content/knowledgebase/misp.md b/src/content/knowledgebase/tool-misp.md
similarity index 98%
rename from src/content/knowledgebase/misp.md
rename to src/content/knowledgebase/tool-misp.md
index f143ff3..fe4815d 100644
--- a/src/content/knowledgebase/misp.md
+++ b/src/content/knowledgebase/tool-misp.md
@@ -3,7 +3,7 @@ title: "MISP - Plattform für Threat Intelligence Sharing"
 tool_name: "MISP"
 description: "Das Rückgrat des modernen Threat-Intelligence-Sharings mit über 40.000 aktiven Instanzen weltweit."
 last_updated: 2025-07-20
-author: "Claude 4 Sonnet"
+author: "Claude 4 Sonnett (Prompt: Mario Stöckl)"
 difficulty: "intermediate"
 categories: ["incident-response", "static-investigations", "malware-analysis", "network-forensics", "cloud-forensics"]
 tags: ["web-based", "threat-intelligence", "api", "correlation", "ioc-sharing", "automation"]
diff --git a/src/content/knowledgebase/nextcloud.md b/src/content/knowledgebase/tool-nextcloud.md
similarity index 98%
rename from src/content/knowledgebase/nextcloud.md
rename to src/content/knowledgebase/tool-nextcloud.md
index 25edc18..2f861b9 100644
--- a/src/content/knowledgebase/nextcloud.md
+++ b/src/content/knowledgebase/tool-nextcloud.md
@@ -3,7 +3,7 @@ title: "Nextcloud - Sichere Kollaborationsplattform"
 tool_name: "Nextcloud"
 description: "Detaillierte Anleitung und Best Practices für Nextcloud in forensischen Einsatzszenarien"
 last_updated: 2025-07-20
-author: "Claude 4 Sonnet"
+author: "Claude 4 Sonnett (Prompt: Mario Stöckl)"
 difficulty: "novice"
 categories: ["collaboration-general"]
 tags: ["web-based", "collaboration", "file-sharing", "api", "encryption", "document-management"]
diff --git a/src/content/knowledgebase/velociraptor.md b/src/content/knowledgebase/tool-velociraptor.md
similarity index 98%
rename from src/content/knowledgebase/velociraptor.md
rename to src/content/knowledgebase/tool-velociraptor.md
index 4499d33..efd55f2 100644
--- a/src/content/knowledgebase/velociraptor.md
+++ b/src/content/knowledgebase/tool-velociraptor.md
@@ -3,7 +3,7 @@ title: "Velociraptor – Skalierbare Endpoint-Forensik mit VQL"
 tool_name: "Velociraptor"
 description: "Detaillierte Anleitung und Best Practices für Velociraptor – Remote-Forensik der nächsten Generation"
 last_updated: 2025-07-20
-author: "Claude 4 Sonnet"
+author: "Claude 4 Sonnett (Prompt: Mario Stöckl)"
 difficulty: "advanced"
 categories: ["incident-response", "malware-analysis", "network-forensics"]
 tags: ["web-based", "endpoint-monitoring", "artifact-extraction", "scripting", "live-forensics", "hunting"]
diff --git a/src/data/tools.yaml b/src/data/tools.yaml
index 808b804..6335317 100644
--- a/src/data/tools.yaml
+++ b/src/data/tools.yaml
@@ -55,28 +55,22 @@ tools:
       - Windows
       - Linux
     accessType: download
-    license: Apache 2.0
+    license: Apache-2.0
     knowledgebase: false
   - name: Volatility 3
     type: software
     description: >-
-      Der Standard für Memory-Forensik - komplett neu entwickelt für moderne
-      Ermittlungen. Version 3 (Feature-Parity Mai 2025) eliminiert das lästige
-      --profile durch automatische OS-Erkennung. Revolutionäre Performance durch
-      symbolbasierte Analyse verarbeitet Multi-GB-Dumps in Sekunden statt
-      Minuten. Über 100 Plugins dekodieren Prozesse, DLL-Injections,
-      Netzwerkverbindungen, Registry-Hives und versteckte Rootkits. Native
-      Python-3-Architektur mit Timeline-Integration und Cloud-Storage-Support
-      (S3, GCS). Neue Plugins: windows.hollowprocesses für Process-Ghosting,
-      linux.ebpf für Kernel-Hooks, windows.shimcachemem für Persistence.
-      YARA-Integration und Sigma-Rules direkt im Framework. Die intuitivere API
-      beschleunigt Plugin-Entwicklung drastisch. Unterstützt aktuelle Windows
-      11, Linux 6.x und macOS Sonoma. Der Umstieg von V2 lohnt sich allein wegen
-      der Performance - Analysen die früher Stunden dauerten sind jetzt in
-      Minuten erledigt.
-    skillLevel: advanced
+      Memory-Forensik-Framework mit automatischer OS-Erkennung für Windows,
+      Linux, macOS RAM-Dumps analysiert über 100 Plugins für Prozess-Extraktion,
+      DLL-Injections, Netzwerk-Sockets, Registry-Hives und Rootkit-Artefakte.
+      Native Python-3-Architektur mit YARA-Integration für
+      Memory-Pattern-Matching und Timeline-Export in strukturierte Formate. Neue
+      Plugins erkennen Process-Hollowing, Kernel-Hooks und
+      Persistence-Mechanismen automatisch. Symbol-basierte Analyse beschleunigt
+      Multi-GB-Dump-Verarbeitung durch optimierte Memory-Parsing-Algorithmen.
+      Cloud-Storage-Support ermöglicht verteilte Analyse großer Memory-Images.
     url: https://www.volatilityfoundation.org/
-    icon: 🧠
+    skillLevel: advanced
     domains:
       - incident-response
       - static-investigations
@@ -85,8 +79,12 @@ tools:
     phases:
       - examination
       - analysis
+    platforms:
+      - Windows
+      - Linux
+      - macOS
     tags:
-      - command-line
+      - cli
       - plugin-support
       - scripting
       - memory-timeline
@@ -98,6 +96,8 @@ tools:
       - python-api
       - scenario:memory_dump
       - opensource
+      - memory-analysis
+      - ram-dump
     related_concepts:
       - Hash Functions & Digital Signatures
       - Regular Expressions (Regex)
@@ -106,29 +106,24 @@ tools:
       - Rekall
       - WinPmem
       - LiME
-    platforms:
-      - Windows
-      - Linux
-      - macOS
-    accessType: download
+    icon: 🧠
     license: VSL
-    knowledgebase: false
+    accessType: download
   - name: MISP
-    icon: 🌐
     type: software
     description: >-
-      Die Threat-Intelligence-Sharing-Plattform vernetzt viele Organisationen
-      weltweit im Kampf gegen Cyberkriminalität. Strukturiertes Teilen von IOCs
-      durch standardisierte Attribute: IP-Adressen, Domains, Hashes, YARA-Rules,
-      Malware-Samples. Die Galaxies und Taxonomien klassifizieren Bedrohungen
-      nach ATT&CK, Kill-Chain oder eigenen Schemata. Föderierte Architektur
-      ermöglicht selektives Sharing zwischen vertrauenswürdigen Partnern.
-      Correlation-Engine findet Zusammenhänge zwischen scheinbar unabhängigen
-      Incidents. Warninglists reduzieren False-Positives durch Whitelisting
-      bekannter Good-Entities. ZeroMQ-Feed pusht IOCs in Echtzeit an Firewalls
-      und SIEMs. Die ausgereiften APIs (REST, PyMISP) automatisieren
-      Threat-Intelligence-Workflows. Export in STIX, OpenIOC und Dutzende andere
-      Formate. Essenziell für proaktive Verteidigung.
+      Threat-Intelligence-Sharing-Platform für strukturiertes IOC-Management
+      durch standardisierte Attribute: IP-Adressen, Domains, Datei-Hashes,
+      YARA-Rules, Malware-Samples mit Metadaten-Anreicherung. Galaxies und
+      Taxonomien klassifizieren Bedrohungen nach MITRE ATT&CK-Framework und
+      Kill-Chain-Phasen. Föderierte Architektur ermöglicht selektives
+      Intelligence-Sharing zwischen vertrauenswürdigen Partnern durch
+      Tagging-System. Correlation-Engine findet automatisch Zusammenhänge
+      zwischen scheinbar unabhängigen Incidents. ZeroMQ-Feed pusht IOCs in
+      Echtzeit an Firewalls, SIEMs und Detection-Systeme für automatisierte
+      Response.
+    url: https://misp-project.org/
+    skillLevel: intermediate
     domains:
       - incident-response
       - static-investigations
@@ -142,13 +137,6 @@ tools:
       - analysis
     platforms:
       - Web
-    skillLevel: intermediate
-    accessType: server-based
-    url: https://misp-project.org/
-    projectUrl: https://misp.cc24.dev
-    license: AGPL-3.0
-    knowledgebase: true
-    statusUrl: https://status.mikoshi.de/api/badge/34/status
     tags:
       - web-interface
       - IOC-matching
@@ -167,6 +155,11 @@ tools:
     related_software:
       - Cortex
       - OpenCTI
+    icon: 🌐
+    projectUrl: https://misp.cc24.dev
+    license: AGPL-3.0
+    accessType: server-based
+    knowledgebase: true
   - name: DFIR-IRIS
     icon: 🌺
     type: software
@@ -208,7 +201,6 @@ tools:
     skillLevel: intermediate
     accessType: server-based
     url: https://dfir-iris.org/
-    projectUrl: ''
     license: LGPL-3.0
     knowledgebase: false
     tags:
@@ -227,23 +219,22 @@ tools:
     related_concepts:
       - Digital Evidence Chain of Custody
   - name: Timesketch
-    icon: ⏱️
     type: software
     description: >-
-      Googles Timeline-Analyse-Platform meistert die Herausforderung, Millionen
-      von Zeitstempeln aus heterogenen Quellen zu korrelieren. Die
-      Elasticsearch-Backend-Architektur ermöglicht Suchen über Mengen
-      forensischer Daten in verhältnismäßig kurzer Zeit.
-      Plaso/Log2timeline-Integration parst automatisch über 300 Log-Formate in
-      eine einheitliche Super-Timeline. Collaborative Investigation durch
-      geteilte Sketches, Kommentare und Saved Searches. Die Timeline-Explorer
-      visualisiert Ereignisse interaktiv mit Heatmaps und Aktivitätsgraphen.
-      Analyzers erkennen Anomalien wie Login-Brute-Force oder
-      Data-Exfiltration-Muster. Sigma-Rules werden direkt auf Timelines
-      angewendet. Stories dokumentieren Findings narrativ für
-      Management-Reports. Die Python-API ermöglicht automatisierte Analysen.
-      Unverzichtbar für Incidents mit komplexen zeitlichen Abläufen über
-      multiple Systeme.
+      Google's Collaborative Timeline-Analyse-Platform meistert Millionen von
+      korrelierten Events durch hochperformante
+      Elasticsearch-Backend-Architektur für Enterprise-Scale-Investigations.
+      Plaso-Integration parst automatisch über 300 verschiedene Log-Formate in
+      einheitliche Super-Timeline mit standardisierten Attributen. Interactive
+      Timeline-Explorer mit dynamischen Heatmaps, Activity-Graphen und
+      Statistical-Analysis für Advanced-Pattern-Recognition. Sigma-Rules werden
+      direkt auf Timelines angewendet für Automated-Threat-Detection,
+      Machine-Learning-Analyzers erkennen Login-Brute-Force, Lateral-Movement
+      und Data-Exfiltration-Patterns. Collaborative-Features: Shared-Sketches,
+      Analyst-Comments, Saved-Searches und narrative Stories für
+      Management-Reporting.
+    url: https://timesketch.org/
+    skillLevel: intermediate
     domains:
       - incident-response
       - static-investigations
@@ -255,18 +246,6 @@ tools:
       - reporting
     platforms:
       - Web
-    related_software:
-      - Plaso (log2timeline)
-      - Elasticsearch
-      - Kibana
-    domain-agnostic-software: null
-    skillLevel: intermediate
-    accessType: server-based
-    url: https://timesketch.org/
-    projectUrl: https://timesketch.cc24.dev
-    license: Apache 2.0
-    knowledgebase: false
-    statusUrl: https://uptime.example.lab/api/badge/3/status
     tags:
       - web-interface
       - timeline
@@ -280,25 +259,30 @@ tools:
       - heatmaps
       - anomaly-detection
       - narrative-documentation
+      - timeline-analysis
+      - collaborative-analysis
     related_concepts:
       - Regular Expressions (Regex)
+    related_software:
+      - Plaso (log2timeline)
+      - Elasticsearch
+      - Kibana
+    icon: ⏱️
+    projectUrl: https://timesketch.cc24.dev
+    license: Apache-2.0
+    accessType: server-based
   - name: Wireshark
-    icon: 🦈
     type: software
     description: >-
-      Der unangefochtene König der Netzwerk-Protokoll-Analyse dekodiert die
-      meisten Netzwerkprotokolle von Ethernet bis zu exotischen ICS-Protokollen.
-      Display-Filter mit mächtiger Syntax ermöglichen chirurgisch präzise
-      Paket-Selektion. Follow-Stream rekonstruiert komplette TCP-Sessions,
-      HTTP-Uploads oder FTP-Transfers. Expert-Info identifiziert Anomalien wie
-      Retransmissions, Malformed Packets oder Protokoll-Violations. Die
-      Statistik-Funktionen visualisieren Conversations, IO-Graphs und
-      Protocol-Hierarchien. Export-Objekte extrahiert übertragene Dateien aus
-      HTTP, SMB oder TFTP. Coloring-Rules heben verdächtige Pakete hervor.
-      GeoIP-Integration zeigt geografische Verbindungsdaten. TLS-Decryption mit
-      Private Keys oder SSLKEYLOGFILE. Die Lua- und C-Plugin-API erlaubt
-      Custom-Dissectors. Unverzichtbar für Malware-C2-Analyse,
-      Data-Exfiltration-Erkennung und Netzwerk-Troubleshooting.
+      Netzwerk-Protokoll-Analyzer dekodiert Ethernet bis zu ICS-Protokollen mit
+      mächtiger 
+        Display-Filter-Syntax für präzise Paket-Selektion. Follow-Stream rekonstruiert komplette 
+        TCP-Sessions, HTTP-Uploads, FTP-Transfers. Expert-Info identifiziert Anomalien wie 
+        Retransmissions, Malformed Packets. Export-Objekte extrahiert übertragene Dateien aus 
+        HTTP, SMB, TFTP. TLS-Decryption mit Private Keys oder SSLKEYLOGFILE. Lua- und C-Plugin-API 
+        für Custom-Dissectors.
+    url: https://www.wireshark.org/
+    skillLevel: intermediate
     domains:
       - incident-response
       - malware-analysis
@@ -313,17 +297,6 @@ tools:
       - Windows
       - Linux
       - macOS
-    related_software:
-      - NetworkMiner
-      - tcpdump
-      - Arkime
-    domain-agnostic-software: null
-    skillLevel: intermediate
-    accessType: download
-    url: https://www.wireshark.org/
-    projectUrl: ''
-    license: GPL-2.0
-    knowledgebase: false
     tags:
       - gui
       - protocol-decode
@@ -337,28 +310,34 @@ tools:
       - statistics
       - coloring-rules
       - tls-decryption
+      - network-analysis
+      - packet-analysis
+      - traffic-analysis
     related_concepts:
       - Regular Expressions (Regex)
+    related_software:
+      - NetworkMiner
+      - tcpdump
+      - Arkime
+    icon: 🦈
+    license: GPL-2.0
+    accessType: download
   - name: Magnet AXIOM
-    icon: 🧲
     type: software
     description: >-
-      Die umfassende Digital-Investigation-Plattform kombiniert Akquisition, 
-      Analyse und Reporting in einer integrierten Suite für
-      End-to-End-Forensik.  AXIOM Process sammelt Artefakte von über 300
-      Datenquellen: Computers,  Mobile Devices, Cloud Services, IoT-Geräte. Die
-      KI-gestützte Artifact- Categorization klassifiziert automatisch
-      verdächtige Inhalte und reduziert  manuelle Review-Zeit drastisch.
-      Besonders wertvoll: Internet Evidence  Finder (IEF) Engine extrahiert
-      Web-Artefakte aus allen Major-Browsern,  Magnet.AI beschleunigt
-      CSAM-Detection, Connections-View visualisiert  Kommunikations-Patterns
-      zwischen Personen. Cloud-Forensics-Module greifen  auf Google, Microsoft,
-      Apple-Accounts zu. Die Parallel-Processing-Engine  analysiert Terabytes in
-      Stunden statt Tagen. Advanced-Carving rekonstruiert  gelöschte
-      Multimedia-Dateien. Timeline-Engine korreliert Events über alle 
-      Evidenzen. Report-Templates generieren gerichtsfeste Dokumentation. 
-      Integration mit AXIOM Cyber für Endpoint-Response. Eine etablierte
-      Software  für High-Volume-Ermittlungen mit Budget für Enterprise-Lizenzen.
+      Umfassende Digital-Investigation-Plattform kombiniert Akquisition, Analyse
+      und Reporting in integrierter Suite für End-to-End-Forensik komplexer
+      Fälle. AXIOM Process sammelt Artefakte von über 300 Datenquellen:
+      Desktop-Computer, Mobile Devices, Cloud Services, IoT-Geräte mit
+      automatischer Parser-Erkennung. KI-gestützte Artifact-Categorization
+      klassifiziert verdächtige Inhalte automatisch und reduziert manuelle
+      Review-Zeit drastisch. Internet Evidence Finder (IEF) Engine extrahiert
+      Web-Artefakte aus allen Major-Browsern, Magnet.AI beschleunigt
+      CSAM-Detection durch Machine Learning. Connections-View visualisiert
+      Kommunikations-Patterns zwischen Personen, Parallel-Processing-Engine
+      analysiert Terabytes in Stunden.
+    url: https://www.magnetforensics.com/products/magnet-axiom/
+    skillLevel: beginner
     domains:
       - incident-response
       - static-investigations
@@ -372,16 +351,6 @@ tools:
       - reporting
     platforms:
       - Windows
-    related_software:
-      - GrayKey
-      - Cellebrite UFED
-    domain-agnostic-software: null
-    skillLevel: beginner
-    accessType: commercial
-    url: https://www.magnetforensics.com/products/magnet-axiom/
-    projectUrl: ''
-    license: Proprietary
-    knowledgebase: false
     tags:
       - gui
       - commercial
@@ -398,22 +367,27 @@ tools:
     related_concepts:
       - Digital Evidence Chain of Custody
       - Hash Functions & Digital Signatures
+    related_software:
+      - GrayKey
+      - Cellebrite UFED
+    icon: 🧲
+    license: Proprietary
+    accessType: commercial
   - name: Cellebrite UFED
-    icon: 📱
     type: software
     description: >-
-      Der de-facto-Behördenstandard der mobilen Forensik greift aktuelle iPhones
-      und Android-Flaggschiffe durch Zero-Day-Exploits und proprietäre
-      Bypass-Methoden an, ist aber auch sehr teuer. Physical Extraction umgeht
-      Verschlüsselung für vollständigen Dateisystem-Zugriff. Logical Plus
-      erweitert Standard-Backups um gelöschte Daten. Advanced Services greifen
-      auf Cellebrites Exploit-Labor für besonders resistente Geräte zurück.
-      Physical Analyzer visualisiert extrahierte Daten intelligent:
-      Timeline-Ansicht, Geo-Location-Maps, Social-Network-Graphen,
-      Kommunikations-Muster. Project-VIC Integration für CSAM-Erkennung.
-      Chain-of-Custody-Dokumentation erfüllt höchste forensische Standards. Die
-      Cloud-Analyzer-Lizenz ermöglicht Zugriff auf über 50 Cloud-Services.
-      Updates alle 3 Monate für neue Geräte und Apps. 
+      Entsperrt aktuelle iPhones und Android-Flaggschiffe durch
+      Zero-Day-Exploits und proprietäre Bypass-Methoden für Physical-Extraction
+      sensibler Daten. Logical Plus erweitert Standard-iTunes-Backups um
+      gelöschte SQLite-Records und App-Sandbox-Inhalte. Advanced Services
+      greifen auf kontinuierlich aktualisiertes Exploit-Labor für neueste und
+      resistente Geräte-Modelle zurück. Physical Analyzer visualisiert
+      extrahierte Daten mit interaktiver Timeline-Ansicht, Geo-Location-Maps und
+      Social-Network-Relationship-Graphen. Project-VIC Integration erkennt CSAM
+      automatisch durch Hash-Matching, Cloud-Analyzer-Lizenz ermöglicht direkten
+      Zugriff auf 50+ Cloud-Services ohne Device-Dependency.
+    url: https://cellebrite.com/en/ufed/
+    skillLevel: beginner
     domains:
       - static-investigations
       - mobile-forensics
@@ -424,17 +398,6 @@ tools:
       - analysis
     platforms:
       - Windows
-    related_software:
-      - Magnet AXIOM
-      - Oxygen Forensic Suite
-      - MSAB XRY
-    domain-agnostic-software: null
-    skillLevel: beginner
-    accessType: commercial
-    url: https://cellebrite.com/en/ufed/
-    projectUrl: ''
-    license: Proprietary
-    knowledgebase: false
     tags:
       - gui
       - commercial
@@ -451,6 +414,13 @@ tools:
     related_concepts:
       - SQL
       - Digital Evidence Chain of Custody
+    related_software:
+      - Magnet AXIOM
+      - Oxygen Forensic Suite
+      - MSAB XRY
+    icon: 📱
+    license: Proprietary
+    accessType: commercial
   - name: Cuckoo Sandbox 3
     icon: 🥚
     type: software
@@ -483,11 +453,9 @@ tools:
       - Volatility 3
       - MISP
       - VirusTotal
-    domain-agnostic-software: null
     skillLevel: advanced
     accessType: server-based
     url: https://github.com/cert-ee/cuckoo3
-    projectUrl: ''
     license: GPL-3.0
     knowledgebase: false
     tags:
@@ -506,24 +474,20 @@ tools:
     related_concepts:
       - Regular Expressions (Regex)
   - name: Ghidra
-    icon: 🔮
     type: software
     description: >-
-      NSAs Reverse-Engineering-Suite demokratisiert Binary-Analyse auf
-      IDA-Pro-Niveau. Der Decompiler transformiert Assembly in lesbaren C-Code
-      für intuitiveres Verständnis von Programm-Logik. Unterstützt über 30
-      Prozessor-Architekturen von x86/x64 über ARM bis zu obskuren
-      Embedded-CPUs. Das Software Reverse Engineering Framework ermöglicht
-      kollaborative Analyse durch Ghidra-Server. PCode als Intermediate Language
-      vereinheitlicht Cross-Architecture-Analysen. Der Function-Graph
-      visualisiert Control-Flow. Data-Type-Manager rekonstruiert Strukturen und
-      Klassen. Script-Manager automatisiert mit Python/Java wiederkehrende
-      Analysen. Version Tracking vergleicht Binaries über Versionen.
-      Cryptographic-Signature-Finder identifiziert Verschlüsselungs-Routinen.
-      Die Extension-Architektur erlaubt Custom-Analyzers. Perfekt für
-      Malware-Dekonstruktion, Vulnerability-Research und Firmware-Analyse. Die
-      steile Lernkurve wird durch exzellente Dokumentation und
-      NSA-Trainingsmaterial gemildert.
+      Reverse-Engineering-Framework mit fortschrittlichem Decompiler für
+      Assembly-zu-C-Code-Transformation und Cross-Architecture-Binary-Analyse
+      von Malware und Firmware. Unterstützt 30+ Prozessor-Architekturen von
+      x86/x64 über ARM, MIPS bis zu exotischen Embedded-CPUs und DSPs.
+      Ghidra-Server ermöglicht kollaborative Team-Analyse mit Versionskontrolle
+      und Shared-Projects. PCode als Intermediate Language vereinheitlicht
+      Multi-Architecture-Analysen durch abstrakte Darstellung. Function-Graph
+      visualisiert Control-Flow interaktiv, Script-Manager automatisiert mit
+      Python/Java komplexe Reverse-Engineering-Tasks. Pattern-Matching
+      identifiziert bekannte Funktionen automatisch.
+    url: https://ghidra-sre.org/
+    skillLevel: expert
     domains:
       - malware-analysis
       - ics-forensics
@@ -534,18 +498,6 @@ tools:
       - Windows
       - Linux
       - macOS
-    related_software:
-      - IDA Pro
-      - Radare2
-      - x64dbg
-      - Binary Ninja
-    domain-agnostic-software: null
-    skillLevel: expert
-    accessType: download
-    url: https://ghidra-sre.org/
-    projectUrl: ''
-    license: Apache 2.0
-    knowledgebase: false
     tags:
       - gui
       - binary-decode
@@ -561,24 +513,30 @@ tools:
       - version-tracking
     related_concepts:
       - Regular Expressions (Regex)
+    related_software:
+      - IDA Pro
+      - Radare2
+      - x64dbg
+      - Binary Ninja
+    icon: 🔮
+    license: Apache-2.0
+    accessType: download
   - name: Plaso (log2timeline)
-    icon: ⏰
     type: software
     description: >-
-      Der industrielle Timeline-Generator extrahiert Zeitstempel aus hunderten
-      Artefakt-Typen für lückenlose Aktivitäts-Rekonstruktion. Parsers für
-      Windows Event Logs, Registry, Prefetch, Browser-History, Mobile Apps,
-      Cloud-Services und Linux-Logs. Die Storage-Architektur verarbeitet
-      Massendaten effizient. Output in standardisierten Formaten für
-      Elasticsearch, Timesketch oder CSV. Besonders wertvoll: Normalisierung
-      verschiedener Zeitstempel-Formate und Zeitzonen in UTC. Filterung nach
-      Zeitraum, Artefakt-Typ oder Keywords. Die modulare Parser-Architektur
-      erlaubt einfache Erweiterung für neue Formate. Psort sortiert und
-      dedupliziert Events. Analysis-Plugins erkennen Anomalien wie Timestomping.
-      Docker-Support vereinfacht Deployment. Die Performance bei sehr großen
-      Datensätzen kann leiden, aber die Vollständigkeit der Timeline ist
-      unübertroffen. Integration mit Timesketch für kollaborative Analyse macht
-      es zum Forensik-Kraftpaket.
+      Industrieller Timeline-Generator extrahiert Zeitstempel aus hunderten
+      heterogener Artefakt-Typen für lückenlose Digital-Activity-Rekonstruktion
+      komplexer Incidents. Spezialisierte Parser für Windows Event Logs,
+      Registry-Hives, Prefetch-Files, Browser-History, Mobile-App-Databases,
+      Cloud-Service-Logs und Linux-System-Logs. Storage-Architektur verarbeitet
+      Massendaten effizient durch SQLite-Backend und Memory-Optimization.
+      Zeitstempel-Normalisierung konvertiert verschiedene Formate und Zeitzonen
+      automatisch in UTC für einheitliche Timeline. Analysis-Plugins erkennen
+      Anti-Forensik-Techniken wie Timestomping und Clock-Manipulation. Modulare
+      Parser-Architektur ermöglicht einfache Erweiterung für proprietäre oder
+      neue Log-Formate.
+    url: https://plaso.readthedocs.io/
+    skillLevel: intermediate
     domains:
       - incident-response
       - static-investigations
@@ -592,19 +550,8 @@ tools:
       - Windows
       - Linux
       - macOS
-    related_software:
-      - Timesketch
-      - Autopsy
-      - Plaso (log2timeline)
-    domain-agnostic-software: null
-    skillLevel: intermediate
-    accessType: download
-    url: https://plaso.readthedocs.io/
-    projectUrl: ''
-    license: Apache 2.0
-    knowledgebase: false
     tags:
-      - command-line
+      - cli
       - timeline
       - log-parser
       - cross-platform
@@ -616,25 +563,29 @@ tools:
       - timestomping-detection
       - modular-parsers
       - batch-processing
+      - timeline-analysis
+      - forensic-timeline
     related_concepts:
       - Regular Expressions (Regex)
+    related_software:
+      - Timesketch
+      - Autopsy
+      - Plaso (log2timeline)
+    icon: ⏰
+    license: Apache-2.0
+    accessType: download
   - name: CyberChef
-    icon: 👨‍🍳
     type: software
     description: >-
-      Das "Schweizer Taschenmesser" der Daten-Manipulation vereint über 400
-      Operations für Encoding, Verschlüsselung, Analyse und Transformation. Die
-      visuelle "Rezept"-Metapher macht komplexe Operationsketten intuitiv:
-      Base64 dekodieren → XOR entschlüsseln → Strings extrahieren → Hashes
-      berechnen. Besonders wertvoll für Malware-Deobfuskation, CTF-Challenges
-      und forensische Datenanalyse. Magic-Mode erkennt automatisch Encodings.
-      Regex-Support für Pattern-Extraktion. Entropy-Visualisierung identifiziert
-      verschlüsselte Bereiche. Die Offline-Fähigkeit (läuft komplett im Browser)
-      macht es DSGVO-konform für sensible Daten. Import/Export von Rezepten für
-      Wiederverwendung. Unterstützt Dateien bis 2GB. Code-Beautifier für
-      JavaScript, JSON, XML. Image-Forensik mit EXIF-Extraktion. Die
-      GitHub-Version kann selbst gehostet werden. Unverzichtbar für jeden
-      digitalen Ermittler.
+      Daten-Manipulations-Framework mit 400+ Operations für Encoding,
+      Verschlüsselung, Analyse und 
+        Transformation durch visuelle Rezept-Verkettung. Base64 dekodieren → XOR entschlüsseln → 
+        Strings extrahieren → Hashes berechnen in einem Workflow. Magic-Mode erkennt automatisch 
+        Encodings, Entropy-Visualisierung identifiziert verschlüsselte Bereiche. Offline-Fähigkeit 
+        im Browser macht es DSGVO-konform für sensible Daten. Import/Export von Rezepten für 
+        Wiederverwendung.
+    url: https://gchq.github.io/CyberChef/
+    skillLevel: beginner
     domains:
       - incident-response
       - static-investigations
@@ -646,15 +597,6 @@ tools:
       - analysis
     platforms:
       - Web
-    related_software:
-      - GCHQ Tools
-    domain-agnostic-software: null
-    skillLevel: beginner
-    accessType: server-based
-    url: https://gchq.github.io/CyberChef/
-    projectUrl: ''
-    license: Apache 2.0
-    knowledgebase: false
     tags:
       - web-interface
       - binary-decode
@@ -666,11 +608,16 @@ tools:
       - magic-detection
       - entropy-analysis
       - recipe-based
-      - offline-capable
+      - offline-mode
       - data-transformation
     related_concepts:
       - Regular Expressions (Regex)
       - Hash Functions & Digital Signatures
+    related_software:
+      - GCHQ Tools
+    icon: 👨‍🍳
+    license: Apache-2.0
+    accessType: server-based
   - name: Velociraptor
     icon: 🦖
     type: software
@@ -709,12 +656,11 @@ tools:
       - GRR Rapid Response
       - osquery
       - KAPE
-    domain-agnostic-software: null
     skillLevel: advanced
     accessType: server-based
     url: https://www.velociraptor.app/
     projectUrl: https://raptor.cc24.dev
-    license: Apache 2.0
+    license: Apache-2.0
     knowledgebase: true
     statusUrl: https://status.mikoshi.de/api/badge/33/status
     tags:
@@ -765,12 +711,10 @@ tools:
       - Velociraptor
       - osquery
       - Rekall
-    domain-agnostic-software: null
     skillLevel: advanced
     accessType: server-based
     url: https://github.com/google/grr
-    projectUrl: ''
-    license: Apache 2.0
+    license: Apache-2.0
     knowledgebase: false
     tags:
       - web-interface
@@ -821,12 +765,10 @@ tools:
       - Elasticsearch
       - Suricata
       - Zeek
-    domain-agnostic-software: null
     skillLevel: expert
     accessType: server-based
     url: https://arkime.com/
-    projectUrl: ''
-    license: Apache 2.0
+    license: Apache-2.0
     knowledgebase: false
     tags:
       - web-interface
@@ -841,23 +783,17 @@ tools:
       - api-driven
       - hunt-jobs
       - spi-graph
-    related_concepts: null
   - name: NetworkMiner
-    icon: ⛏️
     type: software
     description: >-
-      Der forensische Netzwerk-Analyzer extrahiert Artefakte aus PCAP-Dateien
-      mit Fokus auf Inhalts-Rekonstruktion statt Paket-Details. Automatische
-      Extraktion von Dateien (Bilder, Dokumente, Executables) aus HTTP, FTP,
-      TFTP und SMB-Traffic. Credentials-Harvesting findet Passwörter in
-      Klartext- Protokollen. Host-Inventar mit OS-Fingerprinting, offenen Ports
-      und Services. DNS-Resolution-Timeline zeigt Domain-Lookups. Keyword-Search
-      über alle extrahierten Inhalte. Die Professional-Version bietet
-      Geo-IP-Lokalisierung, VoIP-Rekonstruktion und Kommandozeilen-Scripting.
-      Besonders wertvoll: Frame-Reassembly auch bei Packet-Loss. Export zu
-      CSV/XML für weitere Analyse. Die intuitive GUI macht es zugänglich für
-      Nicht-Netzwerk-Spezialisten. Limitiert bei verschlüsseltem Traffic, glänzt
-      bei Klartext-Protokollen und Malware-C2-Analyse.
+      PCAP-Analyzer extrahiert Dateien, Credentials und Host-Informationen aus
+      Netzwerk-Traffic 
+        ohne Paket-Level-Details. Automatische Extraktion von Bildern, Dokumenten, Executables aus 
+        HTTP, FTP, TFTP, SMB-Traffic. Credentials-Harvesting findet Passwörter in Klartext-Protokollen. 
+        Host-Inventar mit OS-Fingerprinting, offenen Ports, Services. DNS-Resolution-Timeline zeigt 
+        Domain-Lookups. Frame-Reassembly auch bei Packet-Loss.
+    url: https://www.netresec.com/?page=NetworkMiner
+    skillLevel: beginner
     domains:
       - incident-response
       - static-investigations
@@ -869,17 +805,6 @@ tools:
     platforms:
       - Windows
       - Linux
-    related_software:
-      - Wireshark
-      - tcpdump
-      - CapLoader
-    domain-agnostic-software: null
-    skillLevel: beginner
-    accessType: download
-    url: https://www.netresec.com/?page=NetworkMiner
-    projectUrl: ''
-    license: GPL-2.0 / Commercial
-    knowledgebase: false
     tags:
       - gui
       - file-reconstruction
@@ -893,7 +818,16 @@ tools:
       - artifact-extraction
       - cleartext-protocols
       - frame-reassembly
-    related_concepts: null
+      - network-analysis
+      - packet-analysis
+      - network-forensics
+    related_software:
+      - Wireshark
+      - tcpdump
+      - CapLoader
+    icon: ⛏️
+    license: GPL-2.0 / Commercial
+    accessType: download
   - name: ExifTool
     icon: 📷
     type: software
@@ -925,15 +859,13 @@ tools:
     related_software:
       - ExifTool
       - FOCA
-    domain-agnostic-software: null
     skillLevel: novice
     accessType: download
     url: https://exiftool.org/
-    projectUrl: ''
     license: Perl Artistic License
     knowledgebase: false
     tags:
-      - command-line
+      - cli
       - metadata-parser
       - geolocation
       - cross-platform
@@ -945,24 +877,18 @@ tools:
       - osint-tool
       - document-forensics
       - multimedia-analysis
-    related_concepts: null
   - name: Chainalysis
     icon: ₿
     type: software
     description: >-
-      Die Blockchain-Intelligence-Plattform ist wohlbekannt in Krypto-Forensik
-      mit einer der größten Attribution-Datenbank weltweit.
-      Clustering-Algorithmen identifizieren Millionen von Services: Exchanges,
-      Darknet-Märkte, Mixer, Ransomware-Wallets, Scams. Reactor visualisiert
-      Transaktionsflüsse mit automatischer Risikobewertung. KYT (Know Your
-      Transaction) für Echtzeit- Compliance. Sanctions Screening gegen OFAC und
-      internationale Listen. Der Investigation-Workflow traced Funds durch Mixer
-      und Tumblers. Intelligente Alerts bei verdächtigen Bewegungen. Court-Ready
-      Reports mit Blockchain-Beweiskette. Die API integriert in bestehende Case-
-      Management-Systeme. Unterstützt Bitcoin, Ethereum, und 100+ andere
-      Blockchains. Trainings und Zertifizierungen für Ermittler. Die
-      Lizenzkosten (sechsstellig) und US-Zentrierung limitieren auf
-      Großorganisationen.
+      Analysiert Blockchain-Transaktionen mit der größten Attribution-Datenbank 
+      weltweit. Clustering-Algorithmen identifizieren Millionen Services: 
+      Exchanges, Darknet-Märkte, Mixer, Ransomware-Wallets. Reactor 
+      visualisiert Transaktionsflüsse mit automatischer Risikobewertung,  KYT
+      ermöglicht Echtzeit-Compliance. Investigation-Workflow traced Funds  durch
+      Mixer, Court-Ready Reports dokumentieren Blockchain-Beweiskette. 
+      Unterstützt Bitcoin, Ethereum und 100+ Blockchains, aber hohe 
+      Lizenzkosten limitieren auf Großorganisationen.
     domains:
       - static-investigations
       - fraud-investigation
@@ -975,11 +901,9 @@ tools:
     related_software:
       - GraphSense
       - Elliptic
-    domain-agnostic-software: null
     skillLevel: intermediate
     accessType: commercial
     url: https://www.chainalysis.com/
-    projectUrl: ''
     license: Proprietary
     knowledgebase: false
     tags:
@@ -995,24 +919,23 @@ tools:
       - risk-assessment
       - multi-blockchain
       - api-integration
-    related_concepts: null
   - name: Neo4j
-    icon: 🕸️
     type: software
     description: >-
-      Die Graph-Datenbank transformiert komplexe Beziehungsgeflechte in
-      verständliche Visualisierungen. Cypher-Query-Language ermöglicht intuitive
-      Abfragen: "MATCH (person)-[:TRANSFERRED_TO]->(account) RETURN *". Perfekt
-      für Fraud-Rings, Social-Networks, Geldwäsche-Netzwerke und
-      Kommunikations-Analysen. Graph-Algorithmen finden kürzeste Pfade,
-      Communities und Influencer. Der Visual-Graph-Explorer macht verborgene
-      Verbindungen sichtbar. Import aus CSV, JSON und relationalen Datenbanken.
-      Die APOC-Bibliothek bietet 450+ Prozeduren für erweiterte Analysen. Bloom
-      visualisiert für nicht-technische Stakeholder. Integration mit
-      Elasticsearch für Volltext-Suche. Multi-Datenbank für Case-Isolation. Die
-      Community-Edition (kostenlos) limitiert auf 1 User und 4 CPU-Cores.
-      Skaliert auf große Mengen von Nodes und Relationships. Unverzichtbar für
-      moderne Financial-Crime-Investigations.
+      Native Graph-Datenbank transformiert komplexe Relationship-Data in
+      intuitive Visualisierungen durch Cypher-Query-Language für forensische
+      Pattern-Detection. Graph-Algorithmen finden kürzeste Pfade zwischen
+      Entities, Community-Detection identifiziert Fraud-Rings und
+      Criminal-Networks automatisch. Visual-Graph-Explorer macht verborgene
+      Multi-Hop-Connections sichtbar für Money-Laundering, Social-Engineering
+      und Organized-Crime-Investigations. APOC-Bibliothek bietet 450+
+      spezialisierte Procedures für Advanced-Analytics: Centrality-Measures,
+      PageRank, Clustering-Coefficients. Bloom-Visualization-Tool für
+      nicht-technische Stakeholder mit Point-and-Click-Exploration. Import aus
+      CSV, JSON und relationalen Datenbanken, Elasticsearch-Integration für
+      Hybrid-Search-Scenarios.
+    url: https://neo4j.com/
+    skillLevel: intermediate
     domains:
       - static-investigations
       - malware-analysis
@@ -1027,18 +950,6 @@ tools:
       - Linux
       - macOS
       - Web
-    related_software:
-      - Maltego
-      - Gephi
-      - Linkurious
-    domain-agnostic-software: null
-    skillLevel: intermediate
-    accessType: server-based
-    url: https://neo4j.com/
-    projectUrl: https://graph.cc24.dev
-    license: GPL-3.0 / Commercial
-    knowledgebase: false
-    statusUrl: https://status.mikoshi.de/api/badge/32/status
     tags:
       - web-interface
       - graph-view
@@ -1054,23 +965,26 @@ tools:
       - import-tools
     related_concepts:
       - SQL
+    related_software:
+      - Maltego
+      - Gephi
+      - Linkurious
+    icon: 🕸️
+    projectUrl: https://graph.cc24.dev
+    license: GPL-3.0 / Commercial
+    accessType: server-based
   - name: QGIS
     icon: 🗺️
     type: software
     description: >-
-      Das Open-Source Geographic Information System verwandelt Forensik-Daten
-      mit Geobezug in aussagekräftige Karten und Analysen. Visualisiert
-      Bewegungsprofile aus Smartphone-GPS, Fahrzeug-Telematik oder
-      Fitness-Trackern. Heatmaps zeigen Aufenthaltsschwerpunkte. Buffer-Analysen
-      identifizieren mögliche Treffpunkte. Die Python-Integration (PyQGIS)
-      automatisiert Massen-Datenverarbeitung. Import aus GPX, KML, CSV mit
-      Koordinaten. Temporal Controller animiert Bewegungen über Zeit. Spatial
-      Queries finden Überschneidungen: "Wer war wann am selben Ort?".
-      OpenStreetMap- Integration für Kontext. 3D-Visualisierung für
-      Drohnen-Flugpfade. Print Composer erstellt gerichtsfeste Karten. Die
-      steile Lernkurve wird durch exzellente Tutorials gemildert. Plugins
-      erweitern für Spezialfälle wie Cell-Tower-Analyse. Unverzichtbar wenn
-      Wo-und-Wann zur Schlüsselfrage wird.
+      Verwandelt Forensik-Daten mit Geobezug in aussagekräftige Karten und 
+      Analysen. Visualisiert Bewegungsprofile aus Smartphone-GPS, 
+      Fahrzeug-Telematik oder Fitness-Trackern durch Heatmaps und 
+      Buffer-Analysen. Python-Integration (PyQGIS) automatisiert 
+      Massen-Datenverarbeitung, Temporal Controller animiert Bewegungen über 
+      Zeit. Spatial Queries finden Überschneidungen: "Wer war wann am selben 
+      Ort?". Print Composer erstellt gerichtsfeste Karten für 
+      Beweisaufbereitung.
     domains:
       - static-investigations
       - fraud-investigation
@@ -1086,11 +1000,9 @@ tools:
     related_software:
       - Google Earth Pro
       - ArcGIS
-    domain-agnostic-software: null
     skillLevel: intermediate
     accessType: download
     url: https://qgis.org/
-    projectUrl: ''
     license: GPL-2.0
     knowledgebase: false
     tags:
@@ -1106,7 +1018,6 @@ tools:
       - 3d-visualization
       - print-composer
       - plugin-ecosystem
-    related_concepts: null
   - name: Binwalk
     icon: 🔬
     type: software
@@ -1137,15 +1048,13 @@ tools:
       - Firmware Analysis Toolkit
       - FACT
       - Ghidra
-    domain-agnostic-software: null
     skillLevel: advanced
     accessType: download
     url: https://github.com/ReFirmLabs/binwalk
-    projectUrl: ''
     license: MIT
     knowledgebase: false
     tags:
-      - command-line
+      - cli
       - firmware-extraction
       - signature-analysis
       - file-carving
@@ -1157,25 +1066,18 @@ tools:
       - hexdump-view
       - plugin-support
       - 3d-entropy
-    related_concepts: null
   - name: Nextcloud
     icon: ☁️
     type: software
     description: >-
-      Die DSGVO-konforme Cloud-Collaboration-Suite für sichere Forensik-Team-
-      Zusammenarbeit. End-to-End-Verschlüsselung schützt sensitive Ermittlungs-
-      daten. File-Sharing mit granularen Berechtigungen und Ablaufdaten.
-      Versionierung dokumentiert alle Änderungen.
-      Collabora/OnlyOffice-Integration für gemeinsame Berichterstellung. Talk
-      ermöglicht verschlüsselte Video- konferenzen für Remote-Teams. Der
-      Kalender koordiniert Ermittlungen. Flow automatisiert Workflows: "Wenn
-      Report fertig → Benachrichtige Team". Audit-Logs für Compliance. External
-      Storage bindet bestehende Shares ein. Die Federation vernetzt mehrere
-      Behörden-Instanzen. Two-Factor-Auth und SSO für Enterprise-Security. Apps
-      erweitern für Passwort-Management, Projekt-Boards und Notizen. Skaliert
-      vom Raspberry Pi für kleine Teams bis zur High-Availability-Cluster. Die
-      Kontrolle über die eigenen Daten macht es zur ersten Wahl für Behörden und
-      Consultants.
+      Ermöglicht sichere Cloud-Collaboration für Forensik-Teams mit 
+      End-to-End-Verschlüsselung und granularen Berechtigungen. File-Sharing 
+      mit Ablaufdaten, Versionierung dokumentiert alle Änderungen, 
+      Collabora/OnlyOffice-Integration unterstützt gemeinsame 
+      Berichterstellung. Talk verschlüsselt Video-Konferenzen, Flow 
+      automatisiert Workflows ("Wenn Report fertig → Benachrichtige Team"). 
+      Audit-Logs für Compliance, Federation vernetzt mehrere 
+      Behörden-Instanzen. DSGVO-konform durch eigene Server-Kontrolle.
     domains:
       - incident-response
       - static-investigations
@@ -1219,18 +1121,18 @@ tools:
   - name: Kali Linux
     type: software
     description: >-
-      Die legendäre Penetration-Testing-Distribution dient Forensikern als
-      Schweizer Taschenmesser mit 600+ vorinstallierten Security-Tools.
-      Live-Boot ermöglicht forensische Untersuchungen ohne Host-Kontamination.
-      Forensics-Mode deaktiviert Auto-Mount und Netzwerk für saubere
-      Akquisition. Die Tool-Kategorien decken alle Phasen ab: Imaging (dc3dd),
-      Carving (Foremost), Memory-Analyse (Volatility), Netzwerk (Wireshark),
-      Mobile (ADB), Krypto (hashcat). Metapackages installieren Tool-Gruppen
-      gezielt. Die Rolling-Release-Natur hält alles aktuell. ARM-Images für
-      Raspberry Pi und Mobile-Forensik. NetHunter bringt Kali aufs Smartphone.
-      Anpassbare ISO-Builds für spezialisierte Teams. Die Dokumentation und
-      Community sind exzellent. Vorsicht: Viele Tools sind offensiv - klare
-      Policies nötig! Der Standard für Incident-Response-Teams weltweit.
+      Debian-basierte Live-Boot-Distribution vereint über 600 Security- und
+      Forensik-Tools für kontaminationsfreie Untersuchungen ohne
+      Host-System-Veränderung. Forensics-Mode deaktiviert automatisches Mounting
+      und Netzwerk-Services für forensisch saubere Evidence-Akquisition.
+      Tool-Kategorien decken alle DFIR-Phasen ab: Disk-Imaging (dc3dd,
+      ddrescue), File-Carving (Foremost, Scalpel), Memory-Analyse (Volatility),
+      Netzwerk-Forensik (Wireshark, tcpdump). Metapackages installieren
+      spezialisierte Tool-Gruppen, ARM-Images unterstützen Raspberry Pi für
+      Mobile- und IoT-Forensik. Persistence-Mode speichert Konfigurationen auf
+      USB-Medien.
+    url: https://kali.org/
+    skillLevel: intermediate
     domains:
       - incident-response
       - static-investigations
@@ -1240,22 +1142,11 @@ tools:
       - mobile-forensics
       - cloud-forensics
       - ics-forensics
-    skillLevel: intermediate
-    url: https://kali.org/
-    icon: 🐉
     platforms:
       - OS
-    accessType: download
-    license: GPL-3.0
-    knowledgebase: true
-    related_software:
-      - Parrot Security OS
-      - SIFT Workstation
-    domain-agnostic-software:
-      - specific-os
     tags:
       - gui
-      - command-line
+      - cli
       - cross-platform
       - live-acquisition
       - write-blocker
@@ -1266,23 +1157,31 @@ tools:
       - nethunter
       - custom-builds
       - rolling-release
-    related_concepts: null
+    domain-agnostic-software:
+      - specific-os
+    related_software:
+      - Parrot Security OS
+      - SIFT Workstation
+    icon: 🐉
+    license: GPL-3.0
+    accessType: download
+    knowledgebase: true
   - name: FTK Imager
-    icon: 💾
     type: software
     description: >-
-      Der Klassiker für Windows-basierte Disk-Akquisition erstellt gerichtsfeste
-      Images mit bewährter Zuverlässigkeit. Unterstützt RAW, SMART, E01 und AFF
-      Formate mit Kompression und Verschlüsselung. Die GUI führt durch den
-      Imaging-Prozess mit Hash-Verifizierung (MD5/SHA1). Preview-Mode ermöglicht
-      Triage ohne Full-Image. Memory-Capture für Live-RAM-Akquisition. Mount als
-      Read-Only für sichere Analyse. Die kostenlose Version deckt
-      Standard-Aufgaben ab, limitiert aber bei erweiterten Features. Besonders
-      geschätzt: Zuverlässigkeit bei defekten Sektoren, detaillierte Logs und
-      breite Gerichtsakzeptanz. Protected-Folder-Viewing für Systemdateien.
-      CLI-Version für Automatisierung. Die proprietäre Natur und Windows-Only
-      sind Nachteile, aber in vielen Labors der De-facto-Standard. Perfekt für
-      Einsteiger und Routine-Akquisitionen.
+      Windows-basierter Disk-Imager mit bewährter Zuverlässigkeit und breiter
+      Gerichtsakzeptanz für Standard-Forensik-Laboratorien erstellt Images in
+      RAW, SMART, E01 und AFF-Formaten. GUI führt systematisch durch
+      Imaging-Prozess mit automatischer Hash-Verifizierung (MD5/SHA1) und
+      Progress-Monitoring. Preview-Mode ermöglicht schnelle Evidence-Triage ohne
+      Full-Image-Creation für Time-Critical-Investigations.
+      Memory-Capture-Functionality für Live-RAM-Akquisition von laufenden
+      Windows-Systemen. Protected-Folder-Viewing umgeht
+      Windows-Sicherheitsbeschränkungen für Systemdateien-Access. Besonders
+      geschätzt: robustes Bad-Sector-Handling, detaillierte Forensik-Logs und
+      etablierte Chain-of-Custody-Procedures.
+    url: https://www.exterro.com/digital-forensics-software/ftk-imager
+    skillLevel: beginner
     domains:
       - static-investigations
       - incident-response
@@ -1290,16 +1189,6 @@ tools:
       - data-collection
     platforms:
       - Windows
-    related_software:
-      - EnCase
-      - X-Ways Forensics
-    domain-agnostic-software: null
-    skillLevel: beginner
-    accessType: download
-    url: https://www.exterro.com/digital-forensics-software/ftk-imager
-    projectUrl: ''
-    license: Proprietary
-    knowledgebase: false
     tags:
       - gui
       - physical-copy
@@ -1313,9 +1202,18 @@ tools:
       - protected-folders
       - cli-available
       - triage
+      - disk-imaging
+      - forensic-imaging
+      - evidence-acquisition
     related_concepts:
       - Hash Functions & Digital Signatures
       - Digital Evidence Chain of Custody
+    related_software:
+      - EnCase
+      - X-Ways Forensics
+    icon: 💾
+    license: Proprietary
+    accessType: download
   - name: YARA
     type: software
     description: >-
@@ -1348,7 +1246,7 @@ tools:
     license: BSD-3-Clause
     knowledgebase: false
     tags:
-      - command-line
+      - cli
       - yara-scan
       - signature-analysis
       - regex-search
@@ -1368,22 +1266,20 @@ tools:
       - Cuckoo Sandbox 3
       - Loki
   - name: X-Ways Forensics
-    icon: 🔬
     type: software
     description: >-
-      Das deutsche Präzisionswerkzeug maximiert Effizienz durch überlegene
-      Performance und durchdachte Workflows. Blitzschnelle Searches in
-      Multi-Terabyte-Images durch optimierte Algorithmen. Simultane Analyse
-      mehrerer Evidenzen spart Zeit. Die Gallery-View mit Skin-Tone-Detection
-      beschleunigt CSAM-Ermittlungen. X-Tensions automatisieren wiederkehrende
-      Aufgaben. Besonders stark: Physical-Search über Sektorgrenzen hinweg,
-      Registry-Report-Generator, Timeline mit Millisekunden-Präzision. Der
-      Hex-Editor zeigt Rohdaten parallel zur interpretierten Ansicht.
-      Template-Support für proprietäre Dateiformate. Die spartanische GUI
-      schreckt Einsteiger ab, aber Profis schätzen die Effizienz. Deutlich
-      günstiger als US-Konkurrenz bei vergleichbarer Funktionalität. Der
-      legendäre Support durch Stefan Fleischmann persönlich. Made in Germany mit
-      Fokus auf Gründlichkeit statt Marketing.
+      Forensik-Suite mit hochoptimierten Algorithmen für
+      Multi-Terabyte-Image-Analyse und simultane Evidence-Verarbeitung mehrerer
+      Datenträger parallel. Gallery-View mit Skin-Tone-Detection für
+      CSAM-Ermittlungen, X-Tensions-Plugin-System automatisiert wiederkehrende
+      Analysen und Custom-Workflows. Physical-Search durchsucht über
+      Sektorgrenzen hinweg, Registry-Report-Generator extrahiert
+      Windows-Artefakte strukturiert. Timeline-Engine mit
+      Millisekunden-Präzision korreliert Events über alle Evidenzen. Hex-Editor
+      zeigt Rohdaten parallel zur interpretierten Ansicht, Template-Support
+      dekodiert proprietäre Dateiformate automatisch.
+    url: https://www.x-ways.net/forensics/
+    skillLevel: expert
     domains:
       - static-investigations
       - incident-response
@@ -1393,17 +1289,6 @@ tools:
       - analysis
     platforms:
       - Windows
-    related_software:
-      - WinHex
-      - EnCase
-      - FTK Imager
-    domain-agnostic-software: null
-    skillLevel: expert
-    accessType: commercial
-    url: https://www.x-ways.net/forensics/
-    projectUrl: ''
-    license: Proprietary
-    knowledgebase: false
     tags:
       - gui
       - commercial
@@ -1419,22 +1304,29 @@ tools:
       - template-support
     related_concepts:
       - Digital Evidence Chain of Custody
+    related_software:
+      - WinHex
+      - EnCase
+      - FTK Imager
+    icon: 🔬
+    license: Proprietary
+    accessType: commercial
   - name: Eric Zimmerman Tools
-    icon: 🧰
     type: software
     description: >-
-      Die Tool-Suite des Windows-Forensik-Gurus Eric Zimmerman dekodiert
-      Windows-Artefakte mit unübertroffener Präzision. Jedes Tool ein
-      Spezialist: ShellBags Explorer zeigt Ordner-Zugriffe, PECmd parst Prefetch
-      für Programm-Ausführungen, AmcacheParser findet Programm- installationen,
-      JumpListExplorer enthüllt Recent Documents. Registry Explorer mit
-      Bookmarks und Plugins. MFTECmd extrahiert NTFS-Metadaten. Timeline
-      Explorer visualisiert CSV-Output aller Tools gemeinsam. KAPE orchestriert
-      die Tool-Collection. Besonders wertvoll: ständige Updates für neue
-      Windows-Versionen und Cloud-Artefakte wie OneDrive. Die
-      Kommandozeilen-Tools ermöglichen Batch-Processing. Kostenlos aber Spenden
-      erwünscht. Die aktive Community im Discord teilt Erfahrungen.
-      Dokumentation durch Cheat-Sheets und Blog-Posts. 
+      Windows-Artefakt-Spezialist-Suite von Eric Zimmerman dekodiert moderne
+      Windows-10/11-Strukturen mit unübertroffener Präzision und Detail-Ebene.
+      ShellBags Explorer rekonstruiert Folder-Access-History, PECmd parst
+      Prefetch-Files für Application-Execution-Evidence, AmcacheParser
+      identifiziert Software-Installation-Timestamps. Registry Explorer mit
+      Advanced-Bookmarks und Live-System-Analysis-Capabilities für
+      Running-System-Forensics. Timeline Explorer korreliert CSV-Output aller
+      Tools in unified Super-Timeline mit Multi-Source-Event-Correlation.
+      KAPE-Integration orchestriert Tool-Collection automatisch, ständige
+      Updates für Windows-11-Features, Cloud-Artefakte (OneDrive, Teams) und
+      Enterprise-Environments.
+    url: https://ericzimmerman.github.io/
+    skillLevel: intermediate
     domains:
       - incident-response
       - static-investigations
@@ -1444,15 +1336,6 @@ tools:
       - analysis
     platforms:
       - Windows
-    related_software:
-      - KAPE
-    domain-agnostic-software: null
-    skillLevel: intermediate
-    accessType: download
-    url: https://ericzimmerman.github.io/
-    projectUrl: ''
-    license: MIT
-    knowledgebase: false
     tags:
       - gui
       - artifact-parser
@@ -1468,8 +1351,15 @@ tools:
       - free-tools
       - scenario:windows-registry
       - scenario:persistence
+      - artifact-extraction
+      - windows-forensics
     related_concepts:
       - Digital Evidence Chain of Custody
+    related_software:
+      - KAPE
+    icon: 🧰
+    license: MIT
+    accessType: download
   - name: Regular Expressions (Regex)
     type: concept
     description: >-
@@ -1633,21 +1523,18 @@ tools:
   - name: MSAB XRY
     type: software
     description: >-
-      Die schwedische Mobile-Forensik-Suite bietet Physical und Logical 
-      Extraction für iOS und Android mit regelmäßigen Exploit-Updates. 
-      Besonders stark bei chinesischen Smartphones (Huawei, Xiaomi) und 
-      speziellen Hardware-Typen. Der Drone-Module extrahiert Flugdaten von  DJI
-      und Parrot. XRY Camera identifiziert Geräte aus Bildern. Die 
-      Analyze-Software visualisiert Kommunikationsmuster und Bewegungs- profile.
-      XAMN-Elements für Link-Analyse zwischen mehreren Geräten. 
-      Cloud-Extraction für 30+ Services. Die PIN-Code-Breaker-Hardware  knackt
-      4-6 stellige Codes. Training und Zertifizierung inklusive.  Preislich mit
-      Cellebrite vergleichbar (15.000€+) aber mit  transparenterer
-      Verkaufspolitik. Updates alle 6-8 Wochen für neue  Apps. EU-basierte
-      Alternative zu US-amerikanischen Lösungen.
-    skillLevel: beginner
+      Mobile-Forensik-Suite mit Spezialisierung auf chinesische Smartphones und
+      Hardware-Vielfalt durch regelmäßige Exploit-Updates alle 6-8 Wochen.
+      Drone-Module extrahiert spezifische Flugdaten von DJI und Parrot-Geräten
+      für Luftfahrt-Ermittlungen, XRY Camera identifiziert Device-Models aus
+      Bildern automatisch. PIN-Code-Breaker-Hardware knackt 4-6 stellige
+      Zugangscodes durch Brute-Force-Methoden physisch. XAMN-Elements analysiert
+      Kommunikations-Verbindungen zwischen mehreren Geräten für komplexe
+      Netzwerk-Forensik. Cloud-Extraction für 30+ Services, EU-basierte
+      Alternative mit transparenterer Verkaufspolitik und GDPR-Compliance im
+      Vergleich zu US-amerikanischen Konkurrenten.
     url: https://www.msab.com/product/xry-extract/
-    icon: 📱
+    skillLevel: beginner
     domains:
       - mobile-forensics
       - static-investigations
@@ -1658,9 +1545,6 @@ tools:
       - analysis
     platforms:
       - Windows
-    accessType: download
-    license: Proprietary
-    knowledgebase: false
     tags:
       - gui
       - commercial
@@ -1681,6 +1565,9 @@ tools:
       - Cellebrite UFED
       - Oxygen Forensic Suite
       - Magnet AXIOM
+    icon: 📱
+    license: Proprietary
+    accessType: download
   - name: OSFMount
     icon: 💿
     type: software
@@ -1708,11 +1595,9 @@ tools:
       - Windows
     related_software:
       - FTK Imager
-    domain-agnostic-software: null
     skillLevel: beginner
     accessType: download
     url: https://www.osforensics.com/tools/mount-disk-images.html
-    projectUrl: ''
     license: Proprietary
     knowledgebase: false
     tags:
@@ -1754,18 +1639,13 @@ tools:
       - malware-analysis
     phases:
       - data-collection
-    platforms: []
     related_software:
       - WinPmem
       - LiME
       - Volatility 3
-    domain-agnostic-software: null
     skillLevel: advanced
-    accessType: null
     url: >-
       https://www.nist.gov/publications/guide-integrating-forensic-techniques-incident-response
-    projectUrl: null
-    license: null
     knowledgebase: false
     tags:
       - live-acquisition
@@ -1803,21 +1683,16 @@ tools:
       - static-investigations
     phases:
       - data-collection
-    platforms: []
     related_software:
       - Android Studio
       - ADB
       - ALEAPP
       - Android Backup Extractor
-    domain-agnostic-software: null
     skillLevel: advanced
-    accessType: null
-    url: https://developer.android.com/studio/command-line/adb
-    projectUrl: null
-    license: null
+    url: https://www.scirp.org/journal/paperinformation?paperid=132631
     knowledgebase: true
     tags:
-      - command-line
+      - cli
       - logical-copy
       - mobile-app-data
       - triage
@@ -1863,15 +1738,13 @@ tools:
       - iLEAPP
       - VLEAPP
       - Autopsy
-    domain-agnostic-software: null
     skillLevel: intermediate
     accessType: download
     url: https://github.com/abrignoni/ALEAPP
-    projectUrl: ''
     license: MIT
     knowledgebase: false
     tags:
-      - command-line
+      - cli
       - mobile-app-data
       - artifact-parser
       - timeline
@@ -1883,6 +1756,8 @@ tools:
       - battery-stats
       - well-being-data
       - cross-platform
+      - artifact-extraction
+      - mobile-analysis
     related_concepts:
       - SQL
   - name: iLEAPP
@@ -1918,15 +1793,13 @@ tools:
       - ALEAPP
       - VLEAPP
       - Cellebrite UFED
-    domain-agnostic-software: null
     skillLevel: intermediate
     accessType: download
     url: https://github.com/abrignoni/iLEAPP
-    projectUrl: ''
     license: MIT
     knowledgebase: false
     tags:
-      - command-line
+      - cli
       - mobile-app-data
       - artifact-parser
       - timeline
@@ -1971,15 +1844,13 @@ tools:
     related_software:
       - ALEAPP
       - iLEAPP
-    domain-agnostic-software: null
     skillLevel: intermediate
     accessType: download
     url: https://github.com/abrignoni/VLEAPP
-    projectUrl: ''
     license: MIT
     knowledgebase: false
     tags:
-      - command-line
+      - cli
       - artifact-parser
       - geolocation
       - timeline
@@ -1991,24 +1862,21 @@ tools:
       - crash-data
       - bluetooth-connections
       - gps-tracking
-    related_concepts: null
   - name: dd
-    icon: 💾
     type: software
     description: >-
-      Das Unix-Urgestein 'data duplicator' ist seit 1974 der minimalistische
-      Standard für bit-genaue Datenträger-Kopien. Keine GUI, keine Extras - nur
-      pure Zuverlässigkeit. Die wichtigsten forensischen Parameter:
-      conv=noerror,sync für defekte Sektoren, bs=4M für optimale Performance,
-      status=progress für Fortschrittsanzeige. Pipe zu Hash-Tools für simultane
-      Verifizierung: dd if=/dev/sda | tee image.raw | md5sum. Die Einfachheit
-      ist die Stärke: funktioniert auf jedem Unix-System ohne Installation.
-      Vorsicht vor klassischen Fehlern: if/of-Verwechslung zerstört Beweise!
-      dcfldd und dc3dd sind forensische Weiterentwicklungen mit eingebauter
-      Hash-Verifizierung. Split-Feature für FAT32-Limits: dd if=/dev/sda | split
-      -b 4G. Network-Imaging via netcat möglich. Der Respekt vor dd trennt
-      Profis von Amateuren - ein falscher Parameter vernichtet unwiederbringlich
-      Daten. Immer noch die Basis vieler kommerzieller Imaging-Tools.
+      Unix-Standard für bit-genaue Datenträger-Kopien seit 1974 mit absolut
+      minimalistischem System-Footprint für kontaminationsfreie Akquisition.
+      Wichtigste forensische Parameter: conv=noerror,sync für graceful
+      Bad-Sector-Handling, bs=4M für optimale I/O-Performance, status=progress
+      für Fortschrittsanzeige. Pipe-Capability zu Hash-Tools ermöglicht
+      simultane Verifizierung ohne Intermediate-Storage. Funktioniert auf jedem
+      Unix-System ohne Installation oder Dependencies. Zero-Kontamination durch
+      Pure-Read-Access ohne Metadata-Modifikation. Network-Imaging via netcat
+      für Remote-Acquisition über SSH-Tunnels. Split-Output für FAT32-Limits,
+      Signal-Handling für graceful Interruption und Resume-Capability.
+    url: https://www.gnu.org/software/coreutils/dd
+    skillLevel: intermediate
     domains:
       - incident-response
       - static-investigations
@@ -2017,19 +1885,8 @@ tools:
     platforms:
       - Linux
       - macOS
-    related_software:
-      - dcfldd
-      - dc3dd
-      - ewfacquire
-    domain-agnostic-software: null
-    skillLevel: intermediate
-    accessType: Linux (GNU-Utils)
-    url: https://www.gnu.org/software/coreutils/dd
-    projectUrl: ''
-    license: GPL-3.0
-    knowledgebase: false
     tags:
-      - command-line
+      - cli
       - physical-copy
       - raw-image-support
       - scenario:disk_imaging
@@ -2041,26 +1898,31 @@ tools:
       - unix-standard
       - minimal-contamination
       - performance-tuning
+      - disk-imaging
+      - bit-copy
     related_concepts:
       - Digital Evidence Chain of Custody
+    related_software:
+      - dcfldd
+      - dc3dd
+      - ewfacquire
+    icon: 💾
+    license: GPL-3.0
   - name: dcfldd
-    icon: 🔐
     type: software
     description: >-
-      Defense Computer Forensics Lab DD erweitert das klassische dd um
-      essenzielle forensische Features. Simultane Hash-Berechnung (MD5, SHA1,
-      SHA256) während des Imaging spart Zeit und garantiert Integrität.
-      Status-Output zeigt Geschwindigkeit, verbleibende Zeit und übertragene
-      Daten. Split-on-the-fly für Multi-Volume-Archives ohne
-      Zwischenspeicherung. Pattern-Wiping für sicheres Löschen nach
-      DoD-Standards. Log-Output dokumentiert jeden Schritt für Chain-of-Custody.
-      Die Verify-Funktion prüft geschriebene Daten sofort. Besonders wertvoll:
-      Hashing einzelner Blöcke für granulare Integritätsprüfung, Fortsetzen
-      unterbrochener Images, mehrere Output-Ziele gleichzeitig. Die forensischen
-      Erweiterungen machen es zur ersten Wahl für professionelle Labore. Syntax
-      bleibt dd-kompatibel für einfachen Umstieg. Performance mit großen
-      Blockgrößen optimiert. Die aktive Entwicklung hält mit modernen
-      Anforderungen Schritt. Standard in vielen Forensik-Distributionen.
+      Defense Computer Forensics Lab DD erweitert klassisches dd um essenzielle
+      forensische Features für professionelle Imaging-Workflows. Simultane
+      Multi-Hash-Berechnung (MD5, SHA1, SHA256) während des Imaging-Prozesses
+      ohne Performance-Einbußen oder zusätzliche Passes. Split-on-the-fly für
+      Multi-Volume-Archives ohne Zwischenspeicherung, Pattern-Wiping für DoD
+      5220.22-M-konforme sichere Löschung. Status-Output zeigt
+      Transfer-Geschwindigkeit, verbleibende Zeit und ETA-Berechnung.
+      Block-Level-Hashing ermöglicht granulare Integritätsprüfung einzelner
+      Disk-Sektoren für Partial-Corruption-Detection. Verify-Funktion prüft
+      geschriebene Daten sofort durch Read-Back-Verification.
+    url: https://github.com/resurrecting-open-source-projects/dcfldd
+    skillLevel: intermediate
     domains:
       - incident-response
       - static-investigations
@@ -2068,19 +1930,8 @@ tools:
       - data-collection
     platforms:
       - Linux
-    related_software:
-      - dd
-      - dc3dd
-      - FTK Imager
-    domain-agnostic-software: null
-    skillLevel: intermediate
-    accessType: download
-    url: https://github.com/resurrecting-open-source-projects/dcfldd
-    projectUrl: ''
-    license: GPL-2.0
-    knowledgebase: false
     tags:
-      - command-line
+      - cli
       - physical-copy
       - hashing
       - scenario:disk_imaging
@@ -2095,21 +1946,29 @@ tools:
     related_concepts:
       - Hash Functions & Digital Signatures
       - Digital Evidence Chain of Custody
+    related_software:
+      - dd
+      - dc3dd
+      - FTK Imager
+    icon: 🔐
+    license: GPL-2.0
+    accessType: download
   - name: ewfacquire
-    icon: 💾
     type: software
     description: >-
-      Command-line Tool aus der libewf-Bibliothek zum Erstellen forensischer
-      Images  im Expert Witness Format (E01/Ex01). Erstellt segmentierte Archive
-      mit  MD5/SHA1-Hash-Verifizierung und optionaler Kompression. Besonders
-      wertvoll  für Linux-basierte Imaging-Workflows ohne GUI-Overhead.
-      Unterstützt  Case-Metadaten, Examiner-Notizen und Error-Granularity für
-      defekte Sektoren.  Die Segment-Größe ist konfigurierbar für verschiedene
-      Storage-Medien.  Integration in Autopsy und andere Tools über libewf.
-      Alternative zu  proprietären Windows-Imaging-Tools für
-      Open-Source-Forensik-Umgebungen.  Besonders geschätzt: Zuverlässigkeit,
-      Cross-Platform-Kompatibilität und  Standards-Compliance für gerichtsfeste
-      Images.
+      Command-line Imaging-Tool aus der libewf-Bibliothek erstellt forensische
+      Images im industry-standard Expert Witness Format (E01/Ex01) mit
+      vollständiger Metadata-Preservation. Generiert segmentierte Archive mit
+      simultaner MD5/SHA1-Hash-Verifizierung und optionaler
+      zlib/bzip2-Kompression für Storage-Optimization. Besonders wertvoll für
+      Linux-basierte Imaging-Workflows ohne GUI-Overhead und Memory-intensive
+      Operations. Unterstützt umfassende Case-Metadaten: Examiner-Informationen,
+      Case-Notizen, Evidence-Description und Chain-of-Custody-Documentation.
+      Error-Granularity für defekte Sektoren mit detailliertem
+      Bad-Block-Logging. Cross-Platform-Alternative zu proprietären
+      Windows-Tools für Open-Source-Forensik-Laboratorien.
+    url: https://github.com/libyal/libewf
+    skillLevel: intermediate
     domains:
       - incident-response
       - static-investigations
@@ -2118,19 +1977,8 @@ tools:
     platforms:
       - Linux
       - macOS
-    related_software:
-      - FTK Imager
-      - EnCase
-      - dd
-    domain-agnostic-software: null
-    skillLevel: intermediate
-    accessType: download
-    url: https://github.com/libyal/libewf
-    projectUrl: ''
-    license: LGPL-3.0
-    knowledgebase: false
     tags:
-      - command-line
+      - cli
       - physical-copy
       - ewf-support
       - compression
@@ -2145,24 +1993,29 @@ tools:
     related_concepts:
       - Hash Functions & Digital Signatures
       - Digital Evidence Chain of Custody
+    related_software:
+      - FTK Imager
+      - EnCase
+      - dd
+    icon: 💾
+    license: LGPL-3.0
+    accessType: download
   - name: Guymager
-    icon: 💿
     type: software
     description: >-
-      Das schlanke Linux-Imaging-Tool maximiert Performance durch intelligentes
-      Multi-Threading und optimierte I/O-Operationen. Die Qt-basierte GUI macht
-      forensisches Imaging auch für Linux-Neulinge zugänglich. Gleichzeitiges
-      Schreiben mehrerer Formate (RAW + EWF) ohne Performance-Verlust. Die
-      eingebaute FIFO-Architektur ermöglicht Pipe-Operationen für
-      Netzwerk-Imaging. Besonders geschätzt: Automatische Badblock-Erkennung mit
-      detailliertem Logging, simultane Hash-Berechnung (MD5, SHA1, SHA256),
-      Fortschrittsbalken mit Zeitschätzung. Die Clone-Funktion dupliziert
-      Datenträger direkt. HPA/DCO-Erkennung warnt vor versteckten Bereichen.
-      Unterstützt USB-Write- Blocker für zusätzliche Sicherheit. Die Performance
-      übertrifft oft kommerzielle Tools bei gleicher Zuverlässigkeit.
-      Debian-Pakete vereinfachen Installation. Der niedrige Ressourcen-Verbrauch
-      erlaubt Imaging auf älteren Systemen. Logging im Detail-Level
-      konfigurierbar für verschiedene Compliance-Anforderungen.
+      Linux-Imaging-Tool maximiert Performance durch intelligentes
+      Multi-Threading und optimierte I/O-Operationen für schnelle
+      Disk-Acquisition. Qt-basierte GUI macht forensisches Imaging auch für
+      Linux-Neulinge zugänglich ohne Command-Line-Expertise. Gleichzeitiges
+      Schreiben mehrerer Output-Formate (RAW + EWF) ohne Performance-Verlust
+      durch parallele Writer-Threads. FIFO-Ring-Buffer-Architektur ermöglicht
+      Pipe-Operationen für Network-Imaging über SSH oder netcat. Automatische
+      Bad-Block-Erkennung mit detailliertem Sector-Error-Logging,
+      HPA/DCO-Detection warnt vor versteckten Disk-Bereichen.
+      USB-Write-Blocker-Support für Hardware-Level-Protection, Resume-Capability
+      für unterbrochene Long-Running-Images.
+    url: https://guymager.sourceforge.io/
+    skillLevel: novice
     domains:
       - incident-response
       - static-investigations
@@ -2170,17 +2023,6 @@ tools:
       - data-collection
     platforms:
       - Linux
-    related_software:
-      - FTK Imager
-      - dc3dd
-      - ddrescue
-    domain-agnostic-software: null
-    skillLevel: novice
-    accessType: download
-    url: https://guymager.sourceforge.io/
-    projectUrl: ''
-    license: GPL-2.0
-    knowledgebase: false
     tags:
       - gui
       - physical-copy
@@ -2197,6 +2039,13 @@ tools:
     related_concepts:
       - Hash Functions & Digital Signatures
       - Digital Evidence Chain of Custody
+    related_software:
+      - FTK Imager
+      - dc3dd
+      - ddrescue
+    icon: 💿
+    license: GPL-2.0
+    accessType: download
   - name: Fuji
     icon: 🗻
     type: software
@@ -2224,15 +2073,13 @@ tools:
     related_software:
       - dd
       - FTK Imager
-    domain-agnostic-software: null
     skillLevel: intermediate
     accessType: download
     url: https://github.com/Lazza/Fuji
-    projectUrl: ''
     license: GPL-3.0
     knowledgebase: false
     tags:
-      - command-line
+      - cli
       - live-acquisition
       - physical-copy
       - apfs
@@ -2271,19 +2118,13 @@ tools:
     phases:
       - data-collection
       - examination
-    platforms: []
     related_software:
       - Aftermath
       - osquery
       - Fuji
-    domain-agnostic-software: null
     skillLevel: intermediate
-    accessType: null
     url: >-
       https://www.sans.org/white-papers/rapid-incident-response-on-macos-actionable-insights-under-hour/
-    projectUrl: null
-    license: null
-    knowledgebase: null
     tags:
       - triage
       - fast-scan
@@ -2329,15 +2170,13 @@ tools:
     related_software:
       - osquery
       - KAPE
-    domain-agnostic-software: null
     skillLevel: intermediate
     accessType: download
     url: https://github.com/jamf/aftermath/
-    projectUrl: ''
-    license: Apache 2.0
+    license: Apache-2.0
     knowledgebase: false
     tags:
-      - command-line
+      - cli
       - triage
       - system-metadata
       - apfs
@@ -2352,24 +2191,21 @@ tools:
     related_concepts:
       - Digital Evidence Chain of Custody
   - name: RegRipper
-    icon: 🔑
     type: software
     description: >-
-      Harlan Carveys Registry-Analyse-Framework revolutioniert Windows-Forensik
-      durch Plugin-basierte Automatisierung. Über 300 Plugins extrahieren
-      spezifische Artefakte aus Registry-Hives: USB-Historie, installierte
-      Software, User-Aktivitäten, Malware-Spuren. Die Perl-basierte Architektur
-      ermöglicht einfache Plugin-Entwicklung für neue Artefakte. Besonders
-      wertvoll: Timeline-Plugins für zeitbasierte Analyse, RegRipper-Reports im
-      strukturierten Format, automatische Korrelation zwischen Hives.
-      Profile-System gruppiert Plugins nach Untersuchungstyp (Malware, User-
-      Activity, Network). Die Community teilt ständig neue Plugins für aktuelle
-      Bedrohungen. Integration mit anderen Tools über CSV-Export. Besonders
-      stark bei APT-Investigations: Services-Analyse, Run-Keys, Scheduled-Tasks.
-      Die Plugin-Dokumentation erklärt forensische Relevanz jedes Artefakts.
-      Version 3.0 modernisiert für aktuelle Windows-Versionen. Der Time-Saver
-      für Registry-Deep-Dives - was manuell Stunden dauert, erledigt RegRipper
-      in Minuten.
+      Windows-Registry-Analyse-Framework mit über 300 spezialisierten Plugins
+      für automatisierte Artefakt-Extraktion: USB-Device-Historie, installierte
+      Software-Timestamps, User-Login-Activity, Malware-Persistence-Mechanisms.
+      Profile-System gruppiert Plugins systematisch nach Untersuchungstyp:
+      Malware-Detection, User-Activity-Reconstruction,
+      Network-Configuration-Analysis. Timeline-Plugins generieren chronologische
+      Registry-Change-Sequences, automatische Korrelation zwischen verschiedenen
+      Hive-Files. Plugin-Dokumentation erklärt forensische Relevanz und
+      Interpretation jedes extrahierten Artefakts detailliert. Active-Community
+      teilt kontinuierlich neue Plugins für emerging Threats und aktuelle
+      Windows-Versionen.
+    url: https://github.com/keydet89/RegRipper3.0
+    skillLevel: intermediate
     domains:
       - incident-response
       - static-investigations
@@ -2380,17 +2216,8 @@ tools:
     platforms:
       - Windows
       - Linux
-    related_software:
-      - Eric Zimmerman Tools
-    domain-agnostic-software: null
-    skillLevel: intermediate
-    accessType: download
-    url: https://github.com/keydet89/RegRipper3.0
-    projectUrl: ''
-    license: MIT
-    knowledgebase: false
     tags:
-      - command-line
+      - cli
       - registry-hives
       - plugin-support
       - scenario:windows-registry
@@ -2405,24 +2232,24 @@ tools:
       - scenario:persistence
     related_concepts:
       - Digital Evidence Chain of Custody
+    related_software:
+      - Eric Zimmerman Tools
+    icon: 🔑
+    license: MIT
+    accessType: download
   - name: Strings
-    icon: 🔤
     type: software
     description: >-
-      Das unterschätzte Basis-Tool extrahiert lesbare ASCII- und Unicode-Strings
-      aus Binärdateien - oft der erste Hinweis auf Malware-Funktionalität.
-      Findet URLs für C2-Server, Hardcoded-Passwords, Pfadangaben,
-      Error-Messages. Die Sysinternals-Version (Windows) bietet GUI und
-      erweiterte Features. GNU-Strings (Linux) integriert perfekt in
-      Pipe-Workflows. Encoding-Options (-e) für Unicode-Varianten essentiell bei
-      modernen Samples. Minimum-Length (-n) filtert Rauschen. Offset-Anzeige
-      (-o) für spätere Hex-Editor-Analyse. Besonders wertvoll: Kombination mit
-      grep für Pattern-Matching, Sort/Uniq für Häufigkeitsanalyse, Integration
-      in automatisierte Malware-Triage. Die Einfachheit täuscht - erfahrene
-      Analysten extrahieren erstaunliche Intelligence. Perfekt für
-      verschlüsselte/gepackte Malware wenn Strings im Unpacking-Stub verbleiben.
-      Output oft Ausgangspunkt für YARA-Rules. Das 5-Minuten-Tool das Stunden
-      detaillierter Analyse spart.
+      Extrahiert lesbare Textfragmente aus Binärdateien für initiale
+      Malware-Analyse und Artefakt-Triage ohne aufwändige
+      Reverse-Engineering-Tools. Findet Command-and-Control-URLs, hardcodierte
+      Passwörter, Dateipfade und Debug-Nachrichten in verschleierten oder
+      gepackten Malware-Samples. Unicode-Unterstützung für internationale
+      Zeichensätze, Offset-Anzeige ermöglicht Hex-Editor-Korrelation.
+      Pipe-Integration mit grep und awk für erweiterte Mustersuche.
+      Unverzichtbar für Quick-Wins bei kryptischen Binärdateien.
+    url: https://docs.microsoft.com/en-us/sysinternals/downloads/strings
+    skillLevel: novice
     domains:
       - incident-response
       - malware-analysis
@@ -2433,16 +2260,8 @@ tools:
       - Windows
       - Linux
       - macOS
-    related_software: null
-    domain-agnostic-software: null
-    skillLevel: novice
-    accessType: Linux (GNU-Utils)
-    url: https://docs.microsoft.com/en-us/sysinternals/downloads/strings
-    projectUrl: ''
-    license: Proprietary/GPL
-    knowledgebase: false
     tags:
-      - command-line
+      - cli
       - string-search
       - binary-decode
       - triage
@@ -2456,24 +2275,24 @@ tools:
       - pipe-friendly
     related_concepts:
       - Regular Expressions (Regex)
+    icon: 🔤
+    license: Proprietary/GPL
   - name: PhotoRec
-    icon: 📸
     type: software
     description: >-
-      Der Datenretter ignoriert Dateisysteme und findet gelöschte Dateien durch
-      Signature-Scanning - selbst nach Formatierung. Über 300 Dateiformate von
-      JPEGs über Office-Dokumente bis zu verschlüsselten Archives. Die
-      Companion- Software TestDisk repariert zusätzlich Partitionstabellen.
-      Arbeitet read-only für forensische Integrität. Besonders stark: Recovery
-      von FAT/NTFS/ext-Systemen, Rettung von SD-Karten und USB-Sticks,
-      funktioniert bei beschädigten Dateisystemen. Die Brute-Force-Methode
-      findet Dateien die andere Tools übersehen. Konfigurierbare Signaturen für
-      spezielle Formate. Der Paranoid-Mode prüft jeden Sektor. Besonders
-      wertvoll für Multimedia-Recovery bei Kinderpornografie-Fällen. Die Text-UI
-      wirkt antiquiert, aber die Effektivität ist unübertroffen. Batch-Mode für
-      automatisierte Recovery. Die freie Lizenz und Cross-Platform-Support
-      machen es zum Standard-Tool weltweit. Oft letzte Hoffnung wenn
-      kommerzielle Tools versagen.
+      Signature-Based File-Carving-Tool rekonstruiert gelöschte Files durch
+      Header/Footer-Pattern-Matching unabhängig vom Dateisystem-Zustand oder
+      Partition-Table-Corruption. Unterstützt über 300 File-Formats: Images
+      (JPEG, PNG, TIFF), Documents (PDF, DOC, XLS), Archives (ZIP, RAR), Videos
+      (AVI, MP4) und Custom-Signatures. Read-Only-Operation gewährleistet
+      forensische Evidence-Integrity, funktioniert bei beschädigten,
+      formatierten oder korrupten Dateisystemen. Paranoid-Mode scannt jeden
+      einzelnen Sektor für Maximum-Recovery-Rate bei fragmentierten Files.
+      Konfigurierbare File-Extensions und Custom-Signature-Development für
+      proprietäre Formats. Companion-Software TestDisk repariert
+      Partition-Tables und Boot-Sectors für Filesystem-Recovery-Scenarios.
+    url: https://www.cgsecurity.org/wiki/PhotoRec
+    skillLevel: beginner
     domains:
       - incident-response
       - static-investigations
@@ -2484,14 +2303,6 @@ tools:
       - Windows
       - Linux
       - macOS
-    related_software: null
-    domain-agnostic-software: null
-    skillLevel: beginner
-    accessType: download
-    url: https://www.cgsecurity.org/wiki/PhotoRec
-    projectUrl: ''
-    license: GPL-2.0
-    knowledgebase: false
     tags:
       - gui
       - file-carving
@@ -2507,24 +2318,26 @@ tools:
       - custom-signatures
     related_concepts:
       - Digital Evidence Chain of Custody
+    icon: 📸
+    license: GPL-2.0
+    accessType: download
   - name: Thumbcache Viewer
-    icon: 🖼️
     type: software
     description: >-
-      Windows speichert Miniaturansichten aller betrachteten Bilder in
-      versteckten thumbcache_*.db Dateien - ein Goldschatz für Forensiker. Das
-      Tool extrahiert diese Thumbnails inklusive EXIF-Zeitstempel, selbst wenn
-      Originalbilder längst gelöscht sind. Beweist unwiderlegbar, dass Bilder
-      auf dem System vorhanden waren. Besonders wertvoll bei CSAM-Ermittlungen
-      und Datendiebstahl. Die verschiedenen Auflösungen (32, 96, 256, 1024)
-      zeigen Detailgrade. Timestamp-Analyse rekonstruiert
-      Betrachtungszeitpunkte. Export als einzelne Bilder oder HTML-Report. Die
-      GUI macht es auch für nicht-technische Ermittler zugänglich. Unterstützt
-      Windows Vista bis 11. Der Batch-Mode verarbeitet mehrere
-      Thumbcache-Dateien. Hash-Matching gegen bekannte CSAM-Datenbanken möglich.
-      Integration mit Timeline-Tools über CSV-Export. Die kostenlose Lizenz
-      demokratisiert wichtige Forensik-Fähigkeiten. Oft der entscheidende Beweis
-      in Kinderschutz-Fällen.
+      Windows-Thumbnail-Cache-Analysis-Tool extrahiert Miniaturansichten aller
+      betrachteten Images aus versteckten thumbcache_*.db-Dateien für
+      Deleted-Content-Recovery. Beweist unwiderlegbar Image-Presence auf System
+      auch nach Original-File-Deletion durch Thumbnail-Persistence in
+      Cache-Database. Multiple-Resolution-Support (32, 96, 256, 1024 Pixel)
+      zeigt verschiedene Detail-Levels und Access-Patterns.
+      EXIF-Metadata-Preservation in Thumbnails ermöglicht GPS-Location-Recovery
+      und Camera-Identification. Timestamp-Analysis rekonstruiert
+      Image-Viewing-Timeline und User-Activity-Patterns. Batch-Processing-Mode
+      für Multiple-Cache-Files, Hash-Export für CSAM-Database-Matching und
+      Content-Correlation. HTML-Report-Generation für Court-Presentation mit
+      Embedded-Thumbnails und Forensic-Metadata-Tables.
+    url: https://thumbcacheviewer.github.io/
+    skillLevel: beginner
     domains:
       - static-investigations
       - fraud-investigation
@@ -2534,14 +2347,6 @@ tools:
       - analysis
     platforms:
       - Windows
-    related_software: null
-    domain-agnostic-software: null
-    skillLevel: beginner
-    accessType: download
-    url: https://thumbcacheviewer.github.io/
-    projectUrl: ''
-    license: GPL-3.0
-    knowledgebase: false
     tags:
       - gui
       - deleted-file-recovery
@@ -2557,6 +2362,9 @@ tools:
       - hash-export
     related_concepts:
       - Digital Evidence Chain of Custody
+    icon: 🖼️
+    license: GPL-3.0
+    accessType: download
   - name: MaxMind GeoIP
     type: software
     description: >-
@@ -2603,48 +2411,35 @@ tools:
       - accuracy-metrics
       - historical-data
       - gdpr-compliant
-    related_concepts: null
-    related_software: null
   - name: SIFT Workstation
     type: software
     description: >-
-      SANS Investigative Forensic Toolkit vereint über 500 Open-Source-Tools in
-      einer kuratierten Ubuntu-Distribution. Rob Lees Vision einer kostenlosen
-      Alternative zu kommerziellen Suiten wurde Realität. Vorinstalliert und
-      konfiguriert: Autopsy, Volatility, Plaso, Registry-Tools,
-      Timeline-Analyzer. Die DFIR-Menüstruktur gruppiert Tools nach
-      Untersuchungsphasen. Besonders wertvoll: Vorkonfigurierte
-      Python-Umgebungen, aktualisierte Tool-Versionen, integrierte
-      Dokumentation. REMnux-Integration für Malware-Analyse. Die VM kann als
-      Appliance oder WSL2 laufen. Regelmäßige Updates durch SANS- Community. Die
-      mitgelieferten Cheat-Sheets beschleunigen Einarbeitung. mount-image-pro
-      automatisiert Evidence-Mounting. SIFT-CLI verwaltet Updates. Die
-      kostenlosen Workbooks führen durch typische Untersuchungen. Performance
-      optimiert für Forensik-Workloads. Die Alternative wenn Budget für
-      kommerzielle Tools fehlt.
+      SANS Investigative Forensic Toolkit vereint über 500 kurierte
+      Open-Source-Tools in optimierter Ubuntu-Distribution für professionelle
+      DFIR-Teams. Vorinstalliert und konfiguriert: Autopsy für Disk-Analysis,
+      Volatility für Memory-Forensik, Plaso für Timeline-Generation,
+      Registry-Tools für Windows-Artefakte. DFIR-Menüstruktur gruppiert Tools
+      logisch nach Untersuchungsphasen und Use-Cases. mount-image-pro
+      automatisiert Evidence-Mounting mit Write-Protection, SIFT-CLI verwaltet
+      Tool-Updates zentral. REMnux-Integration für nahtlose
+      Malware-Analyse-Workflows, kostenlose Workbooks und Cheat-Sheets führen
+      durch typische Untersuchungsszenarien. VM-Images und WSL2-Support für
+      flexible Deployment-Optionen.
+    url: https://www.sans.org/tools/sift-workstation/
+    skillLevel: intermediate
     domains:
       - incident-response
       - static-investigations
       - malware-analysis
       - network-forensics
       - mobile-forensics
-    skillLevel: intermediate
-    url: https://www.sans.org/tools/sift-workstation/
-    icon: 🧰
     platforms:
       - OS
-    accessType: download
-    license: Free / Mixed
-    knowledgebase: false
-    related_software:
-      - REMnux
-      - CAINE
-      - Kali Linux
     domain-agnostic-software:
       - specific-os
     tags:
       - gui
-      - command-line
+      - cli
       - cross-platform
       - write-blocker
       - live-acquisition
@@ -2655,40 +2450,36 @@ tools:
       - vm-ready
       - wsl2-compatible
       - community-maintained
-    related_concepts: null
+    related_software:
+      - REMnux
+      - CAINE
+      - Kali Linux
+    icon: 🧰
+    license: Free / Mixed
+    accessType: download
   - name: Tsurugi Linux
     type: software
     description: >-
-      Die Forensik-Distribution kombiniert Tools für ultimative
-      Ermittlungs-Power. Spezialisiert auf Mobile- und Malware-Forensik mit
-      einzigartigen Features. Der integrierte Hardware-Write-Blocker verhindert
-      Beweis-Kontamination. Bento-Menü organisiert Tools nach japanischer
-      Effizienz-Philosophie. Besonders stark: Android-Forensik-Suite mit
-      ADB-Automatisierung, iOS-Backup-Analyzer, umfangreiche Malware-Sandbox.
-      Die Acquire-Edition ist optimiert für schnelles Imaging mit minimalem RAM.
-      Performance- Kernel speziell für Forensik-Hardware. Einzigartig:
-      Integrierte Übersetzungs-Tools für internationale Ermittlungen,
-      Unterstützung asiatischer Zeichensätze, LINE-Messenger-Parser. Die
-      64-Bit-Only- Architektur nutzt modernen RAM voll aus. Live-Patching hält
-      Tools aktuell ohne Neustart. Der Stealth-Mode deaktiviert alle Netzwerk-
-      Interfaces. Die Alternative für Ermittler die mehr als
-      Standard-Distributionen wollen.
+      Spezialisierte Forensik-Distribution kombiniert Mobile- und
+      Malware-Analyse-Tools mit einzigartigen Hardware-Integration-Features für
+      professionelle Ermittlungen. Integrierter Hardware-Write-Blocker
+      verhindert Evidence-Kontamination automatisch, Bento-Menü organisiert
+      Tools nach japanischer Effizienz-Philosophie systematisch.
+      Android-Forensik-Suite mit ADB-Automatisierung und Root-Detection,
+      iOS-Backup-Analyzer mit Keychain-Extraktion, umfangreiche Malware-Sandbox
+      für Dynamic-Analysis. Performance-Kernel speziell für Forensik-Hardware
+      optimiert, 64-Bit-Only-Architektur nutzt modernen RAM voll aus.
+      Stealth-Mode deaktiviert alle Netzwerk-Interfaces, Unterstützung
+      asiatischer Zeichensätze für internationale Ermittlungen.
+    url: https://tsurugi-linux.org/
+    skillLevel: intermediate
     domains:
       - incident-response
       - static-investigations
       - malware-analysis
       - mobile-forensics
-    skillLevel: intermediate
-    url: https://tsurugi-linux.org/
-    icon: ⛩️
     platforms:
       - OS
-    accessType: download
-    license: GPL / Mixed
-    knowledgebase: false
-    related_software:
-      - CAINE
-      - Kali Linux
     domain-agnostic-software:
       - specific-os
     tags:
@@ -2704,44 +2495,38 @@ tools:
       - hardware-writeblock
       - performance-kernel
       - stealth-mode
-    related_concepts: null
+    related_software:
+      - CAINE
+      - Kali Linux
+    icon: ⛩️
+    license: GPL / Mixed
+    accessType: download
   - name: Parrot Security OS
     type: software
     description: >-
-      Die Privacy-fokussierte Alternative zu Kali Linux mit eingebautem
-      Anonymisierungs-Framework. AnonSurf routet Traffic durch Tor für verdeckte
-      Ermittlungen. Forensik-Tools treffen auf Pentesting-Arsenal in
-      datenschutzfreundlicher Umgebung. Rolling-Release hält 600+ Tools aktuell
-      ohne Neuinstallation. Der ressourcenschonende MATE-Desktop läuft flüssig
-      auf älteren Laptops. Besonders wertvoll: Eingebaute Kryptographie-Tools,
-      sichere Kommunikations-Apps, Sandbox für Malware. Die Forensik-Edition
-      fokussiert auf Incident-Response. Docker-Support für Tool-Isolation.
-      ARM-Versionen für Raspberry Pi und Mobile-Forensik. Die italienische
-      Entwicklung bringt europäische Datenschutz-Werte. Mehrere
-      Desktop-Umgebungen wählbar. Live-Boot mit Persistence für Feldarbeit. Der
-      AppArmor-Schutz härtet gegen Exploits. Community kleiner aber engagierter
-      als Kali. Perfekt für Ermittler die Privatsphäre und Sicherheit
-      priorisieren.
+      Privacy-fokussierte Forensik-Distribution mit eingebautem
+      Anonymisierungs-Framework für verdeckte Ermittlungen und
+      Undercover-Operations. AnonSurf routet kompletten Traffic durch
+      Tor-Netzwerk mit DNS-Leak-Protection, AppArmor-Mandatory-Access-Control
+      härtet System gegen Exploits und Malware. Rolling-Release-Model hält 600+
+      Tools kontinuierlich aktuell ohne komplette Neuinstallation.
+      Ressourcenschonender MATE-Desktop läuft flüssig auf älteren
+      Forensik-Laptops und Field-Equipment. Docker-Support für sichere
+      Tool-Isolation und Malware-Sandboxing, ARM-Versionen für Raspberry Pi und
+      Mobile-Forensik-Einsätze. Live-Boot mit Encrypted-Persistence für sichere
+      Feldarbeit.
+    url: https://parrotsec.org/
+    skillLevel: intermediate
     domains:
       - incident-response
       - static-investigations
       - malware-analysis
       - network-forensics
-    skillLevel: intermediate
-    url: https://parrotsec.org/
-    icon: 🦜
     platforms:
       - OS
-    accessType: download
-    license: GPL-3.0
-    knowledgebase: false
-    related_software:
-      - Kali Linux
-    domain-agnostic-software:
-      - specific-os
     tags:
       - gui
-      - command-line
+      - cli
       - live-acquisition
       - encrypted-traffic
       - secure-sharing
@@ -2752,25 +2537,26 @@ tools:
       - lightweight
       - arm-support
       - docker-ready
-    related_concepts: null
+    related_software:
+      - Kali Linux
+    icon: 🦜
+    license: GPL-3.0
+    accessType: download
+    domain-agnostic-software:
+      - specific-os
   - name: LibreOffice
-    icon: 📄
     type: software
     description: >-
-      Die freie Office-Suite ist mehr als nur Dokumentenerstellung - ein
-      unterschätztes Forensik-Werkzeug. Calc verarbeitet massive CSV-Logs und
-      Datenbank-Exporte mit Pivot-Tabellen und Filtern. Die Makro-Sprache
-      automatisiert wiederkehrende Analysen. Writer erstellt gerichtsfeste
-      Reports mit Inhaltsverzeichnis und Querverweisen. Besonders wertvoll:
-      Import obskurer Dateiformate die Microsoft Office ablehnt, Reparatur
-      korrupter Dokumente, Metadaten-Anzeige fremder Files. Die Version-History
-      in Dokumenten enthüllt Bearbeitungen. Draw visualisiert Netzwerk-Diagramme
-      und Timelines. Base als Frontend für SQLite-Forensik-Datenbanken. Die
-      PDF-Export-Optionen mit Sicherheitseinstellungen für vertrauliche Reports.
-      Kompatibilität mit allen gängigen Formaten. Die Portable-Version läuft von
-      USB ohne Installation. Extensions erweitern für spezielle Aufgaben.
-      Kostenlos aber professionell - spart Lizenzbudget für Spezial-Tools. Der
-      Standard für Forensik-Dokumentation weltweit.
+      Analysiert umfangreiche Ermittlungsdaten durch leistungsstarke
+      Pivot-Tabellen und Filteroptionen für forensische Auswertungen.
+      Verarbeitet CSV-Logs, Datenbank-Exporte und Timeline-Daten für komplexe
+      Korrelationsanalysen. Calc erstellt aussagekräftige Diagramme aus
+      Beweismitteln, Writer generiert gerichtsfeste Berichte mit strukturiertem
+      Inhaltsverzeichnis. Importiert beschädigte oder proprietäre Dateiformate,
+      die kommerzielle Office-Pakete ablehnen. Makro-Sprache automatisiert
+      wiederkehrende Analysen.
+    url: https://www.libreoffice.org/
+    skillLevel: novice
     domains:
       - incident-response
       - static-investigations
@@ -2788,16 +2574,6 @@ tools:
       - Windows
       - Linux
       - macOS
-    related_software:
-      - Microsoft Office 365
-    domain-agnostic-software:
-      - collaboration-general
-    skillLevel: novice
-    accessType: download
-    url: https://www.libreoffice.org/
-    projectUrl: ''
-    license: Mozilla Public License Version 2.0
-    knowledgebase: false
     tags:
       - gui
       - reporting
@@ -2811,24 +2587,23 @@ tools:
       - format-converter
       - pdf-creation
       - free-alternative
-    related_concepts: null
+    related_software:
+      - Microsoft Office 365
+    icon: 📄
+    license: Mozilla Public License Version 2.0
+    accessType: download
   - name: Microsoft Office 365
     type: software
     description: >-
-      Der Industriestandard bietet mehr als Textverarbeitung - ein mächtiges
-      Forensik-Analyse-Toolkit. Excel's Power Query transformiert komplexe
-      Log-Dateien in aussagekräftige Visualisierungen. Power Pivot verarbeitet
-      Millionen Datensätze für Big-Data-Forensik. Die Kollaborations-Features
-      ermöglichen Echtzeit-Teamarbeit an Ermittlungen. Besonders wertvoll:
-      Cloud-Storage für große Beweis-Sammlungen, Version-History für
-      Audit-Trails, Advanced eDiscovery für Compliance. Teams integriert sichere
-      Kommunikation. Der Compliance-Manager dokumentiert Datenschutz-konform.
-      OneNote sammelt Notizen und Screenshots strukturiert. Die KI-Features in
-      Editor verbessern Report-Qualität. Makros automatisieren Routine-Analysen.
-      Die Mobile-Apps ermöglichen Feld-Dokumentation. Power Automate verbindet
-      mit Forensik-Tools. Der Industriestandard hat seinen Preis, aber die
-      Integration und Support sind unübertroffen. Vorsicht: Cloud-Storage kann
-      Datenschutz-problematisch sein für sensible Ermittlungen.
+      Transformiert forensische Massendaten durch Power Query in strukturierte
+      Analysen für umfangreiche Ermittlungen. Power Pivot verarbeitet Millionen
+      von Log-Einträgen, Excel-Makros automatisieren wiederkehrende
+      Auswertungen. Kollaborationsfunktionen ermöglichen Echtzeit-Teamarbeit bei
+      komplexen Fällen, Cloud-Speicher sichert große Beweissammlungen. Advanced
+      eDiscovery für Compliance-Untersuchungen, OneNote strukturiert Screenshots
+      und Notizen. KI-gestützte Editorfunktionen verbessern Berichtsqualität.
+    url: https://www.office.com/
+    skillLevel: novice
     domains:
       - incident-response
       - static-investigations
@@ -2842,20 +2617,10 @@ tools:
       - examination
       - analysis
       - reporting
-    skillLevel: novice
-    url: https://www.office.com/
-    icon: 📊
     platforms:
       - Windows
       - macOS
       - Web
-    accessType: commercial
-    license: Proprietary
-    knowledgebase: false
-    related_software:
-      - LibreOffice
-    domain-agnostic-software:
-      - collaboration-general
     tags:
       - gui
       - web-interface
@@ -2869,26 +2634,28 @@ tools:
       - team-integration
       - mobile-apps
       - automation-capable
-    related_concepts: null
+    related_software:
+      - LibreOffice
+    icon: 📊
+    license: Proprietary
+    accessType: commercial
   - name: EnCase
-    icon: 🔍
     type: software
     description: >-
-      Der Veteran der digitalen Forensik seit 1997 - in vielen Behörden noch
-      immer verwendet. EnScript-Programmierung ermöglicht komplexe
-      Automatisierungen die andere Tools nicht bieten. Die Gerichtserfahrung ist
-      unübertroffen - tausende erfolgreiche Verfahren weltweit. Version 21
-      modernisiert die alternde Architektur mit Cloud-Forensik und
-      Mobile-Support. Besonders stark: Evidence-Processor für
-      Batch-Verarbeitung, umfangreiche Dateisystem-Unterstützung, integrierte
-      Hex-Ansicht. Die EnCE-Zertifizierung öffnet Karrieretüren.
-      Physical-Analyzer-Modul für Smartphones konkurriert mit Cellebrite. Die
-      Case-Management-Features skalieren auf Großverfahren. Nachteile: Hohe
-      Kosten (20.000€+), steile Lernkurve, Performance-Probleme bei großen
-      Images. OpenText-Übernahme bringt Unsicherheit. Der Dongle-Schutz nervt im
-      Feldeinsatz. Viele wechseln zu moderneren Alternativen, aber für
-      EnScript-Power-User noch immer unverzichtbar. Das Prestige-Tool das
-      langsam seine Krone verliert.
+      Etablierte Forensik-Suite für Enterprise-Level-Investigations kombiniert
+      Disk-Imaging, Evidence-Processing und Case-Management in integrierter
+      Plattform. EnScript-Programmiersprache automatisiert komplexe Analysen und
+      Custom-Workflows für wiederkehrende Untersuchungsschritte.
+      Evidence-Processor verarbeitet Batch-Analysen multipler Images parallel,
+      Timeline-Engine korreliert Events über alle Evidenzen.
+      Physical-Analyzer-Modul extrahiert Smartphone-Daten durch Logical- und
+      Physical-Acquisition-Methoden. Hash-Library-Integration für
+      Known-Bad-Detection und NSRL-Filtering. Case-Management skaliert auf
+      Großverfahren mit gerichtsfester Dokumentation und Audit-Trails.
+      Dongle-Schutz und hohe Lizenzkosten (20.000€+) limitieren auf
+      Enterprise-Umgebungen.
+    url: https://www.opentext.com/products/encase-forensic
+    skillLevel: intermediate
     domains:
       - static-investigations
       - incident-response
@@ -2900,17 +2667,6 @@ tools:
       - reporting
     platforms:
       - Windows
-    related_software:
-      - FTK Imager
-      - X-Ways Forensics
-      - Autopsy
-    domain-agnostic-software: null
-    skillLevel: intermediate
-    accessType: commercial
-    url: https://www.opentext.com/products/encase-forensic
-    projectUrl: ''
-    license: Proprietary
-    knowledgebase: false
     tags:
       - gui
       - commercial
@@ -2926,24 +2682,24 @@ tools:
       - enterprise-scale
     related_concepts:
       - Digital Evidence Chain of Custody
+    related_software:
+      - FTK Imager
+      - X-Ways Forensics
+      - Autopsy
+    icon: 🔍
+    license: Proprietary
+    accessType: commercial
   - name: FRED
-    icon: 🖥️
     type: software
     description: >-
-      Forensic Recovery of Evidence Device - die High-End-Hardware-Lösung für
-      professionelle Forensik-Labore. Kombiniert Workstation, Imager und
-      Write-Blocker in einem System. Die UltraBay-Technologie ermöglicht
-      Hot-Swap von bis zu 8 Laufwerken gleichzeitig. Eingebaute Hardware-
-      Write-Blocker für alle gängigen Interfaces: SATA, SAS, IDE, USB, FireWire.
-      Die RAID-Rekonstruktion arbeitet mit defekten Arrays. Besonders wertvoll:
-      Paralleles Imaging mehrerer Evidenzen, Hardware- beschleunigte Hashing,
-      Unterstützung exotischer Formate. Die Touchscreen- Konsole steuert
-      Imaging-Vorgänge. Field-Kit-Version für Vor-Ort-Einsätze. Integration mit
-      FTK, EnCase, X-Ways. Die Workstation-Komponente analysiert während des
-      Imaging. Der hohe Preis macht es zur Investition für High-Volume-Labs. Der
-      Support durch Digital Intelligence ist erstklassig. Die modulare Bauweise
-      erlaubt Upgrades. Für kleine Teams Overkill, für Behörden-Labs oft
-      Standard. Die Hardware-Zuverlässigkeit rechtfertigt den Premium-Preis.
+      Hardware-Forensik-Workstation ermöglicht simultanes Imaging von 8
+      Evidenzen durch Hot-Swap-UltraBay 
+        und integrierte Write-Blocker für SATA/IDE/USB/FireWire. Hardware-Hash-Acceleration beschleunigt 
+        MD5/SHA-Verifizierung, Touchscreen-Konsole steuert Parallel-Processing ohne Host-System-Belastung. 
+        Field-Kit-Version mit 4-Bay-Kapazität für Vor-Ort-Akquisition, modulares Design erlaubt 
+        RAID-Controller-Upgrades für NAS-Forensik.
+    url: https://www.digitalintelligence.com/products/fred/
+    skillLevel: intermediate
     domains:
       - static-investigations
       - incident-response
@@ -2951,14 +2707,6 @@ tools:
       - data-collection
     platforms:
       - Hardware
-    related_software: null
-    domain-agnostic-software: null
-    skillLevel: intermediate
-    accessType: commercial
-    url: https://www.digitalintelligence.com/products/fred/
-    projectUrl: ''
-    license: Proprietary
-    knowledgebase: false
     tags:
       - gui
       - commercial
@@ -2974,6 +2722,9 @@ tools:
       - lab-equipment
     related_concepts:
       - Digital Evidence Chain of Custody
+    icon: 🖥️
+    license: Proprietary
+    accessType: commercial
   - name: GraphSense
     icon: 📊
     type: software
@@ -3006,11 +2757,9 @@ tools:
       - Chainalysis
       - Maltego
       - Neo4j
-    domain-agnostic-software: null
     skillLevel: intermediate
     accessType: server-based
     url: https://graphsense.org/
-    projectUrl: ''
     license: MIT
     knowledgebase: false
     tags:
@@ -3056,7 +2805,6 @@ tools:
       - reporting
     platforms:
       - Web
-    related_software: null
     domain-agnostic-software:
       - collaboration-general
     skillLevel: beginner
@@ -3064,7 +2812,6 @@ tools:
     url: https://gitea.io/
     projectUrl: https://git.cc24.dev
     license: MIT
-    knowledgebase: null
     statusUrl: https://status.mikoshi.de/api/badge/18/status
     tags:
       - web-interface
@@ -3119,7 +2866,7 @@ tools:
     license: MIT
     knowledgebase: false
     tags:
-      - command-line
+      - cli
       - system-metadata
       - artifact-parser
       - cross-platform
@@ -3165,16 +2912,13 @@ tools:
       - Linux
       - Windows
       - macOS
-    related_software: null
-    domain-agnostic-software: null
     skillLevel: advanced
     accessType: download
     url: https://github.com/SecureAuthCorp/impacket
-    projectUrl: ''
-    license: Apache 2.0
+    license: Apache-2.0
     knowledgebase: false
     tags:
-      - command-line
+      - cli
       - remote-collection
       - scripting
       - scenario:persistence
@@ -3220,11 +2964,9 @@ tools:
       - Aircrack-ng
       - WiFi Pineapple
       - Wireshark
-    domain-agnostic-software: null
     skillLevel: advanced
     accessType: download
     url: https://www.kismetwireless.net/
-    projectUrl: ''
     license: GPL-2.0
     knowledgebase: false
     tags:
@@ -3240,76 +2982,64 @@ tools:
       - distributed-sensors
       - api-enabled
       - wardrive-capable
-    related_concepts: null
   - name: ArcGIS
     type: software
     description: >-
-      Esris kommerzielle GIS-Plattform transformiert forensische Geodaten in 
-      aussagekräftige räumliche Analysen für Ermittlungen. Besonders wertvoll
-      für  komplexe Bewegungsprofile aus GPS-Logs, Cell-Tower-Triangulation und 
-      Fahrzeug-Telematik. Die ArcMap-Desktop-Anwendung bietet erweiterte 
-      Spatial-Queries: Buffer-Analysen identifizieren Verdächtige in
-      Tatort-Nähe,  Hot-Spot-Detection findet Kriminalitätsschwerpunkte,
-      Network-Analyst  berechnet optimale Fluchtrouten. Integration mit
-      Datenbanken ermöglicht  Korrelation von Orten mit Personen, Fahrzeugen,
-      Kommunikation. Die  3D-Scene-Funktionen rekonstruieren Tatorte
-      photorealistisch. Crime-Mapping- Extensions speziell für Strafverfolgung.
-      Der Enterprise-Fokus bedeutet  hohe Kosten (ab 7.000€) und
-      Schulungsaufwand, aber bewährte Kompetenz für Geo-Intelligence. 
-    skillLevel: intermediate
+      Transformiert GPS-Daten, Mobilfunk-Logs und Fahrzeug-Telematik in
+      aussagekräftige räumliche Analysen für Ermittlungen. Buffer-Analysen
+      identifizieren Verdächtige in Tatort-Nähe, Hot-Spot-Detection findet
+      Kriminalitätsschwerpunkte. Network-Analyst berechnet optimale
+      Fluchtrouten, 3D-Szenen rekonstruieren Tatorte photorealistisch.
+      Korreliert Geodaten mit Personen, Fahrzeugen und Kommunikationsmustern.
+      Crime-Mapping-Erweiterungen speziell für Strafverfolgung entwickelt.
     url: https://www.esri.com/arcgis
-    icon: 🌍
+    skillLevel: intermediate
     domains:
       - fraud-investigation
       - network-forensics
     phases:
       - reporting
-    tags:
-      - mapping
-      - visualization
-    related_concepts: null
     platforms:
       - Windows
       - macOS
-    accessType: commercial
+    tags:
+      - mapping
+      - visualization
+    icon: 🌍
     license: Proprietary
-    knowledgebase: false
+    accessType: commercial
   - name: Binary Ninja
     type: software
     description: >-
-      Vector 35s moderne Reverse-Engineering-Plattform kombiniert traditionelle 
-      Disassembly mit fortschrittlicher Program-Analysis. Die Multi-Level-IR 
-      (Intermediate Representation) ermöglicht Cross-Architecture-Analysen
-      durch  einheitliche Abstraktion. Besonders innovativ: Der Decompiler
-      erzeugt  lesbaren High-Level-Code, Type-Recovery rekonstruiert
-      Datenstrukturen  automatisch, die Plugin-API (Python/C++) erlaubt tiefe
-      Customization.  Cloud-Integration synchronisiert Analysen zwischen Teams.
-      Die moderne GUI  mit Multiple-Workspaces übertrifft IDA in Usability.
-      Debugger-Integration  für Dynamic-Analysis. Scripting-Console für
-      Ad-hoc-Automation. Die  Personal-License (399€) macht professionelle
-      Binary-Analysis erschwinglich.  Commercial-Lizenzen bieten Team-Features
-      und Cloud-Sync. Besonders stark  bei modernen Architekturen (ARM64,
-      RISC-V) die IDA vernachlässigt.  Die Zukunft des Reverse Engineering für
-      eine neue Generation von Analysten.
-    skillLevel: advanced
+      Moderne Reverse-Engineering-Plattform kombiniert traditionelle Disassembly
+      mit fortschrittlicher Program-Analysis durch innovative
+      Multi-Level-IR-Architecture (Intermediate Representation).
+      Cross-Architecture-Analysis durch einheitliche Abstraktion verschiedener
+      Instruction-Sets und Calling-Conventions. Advanced-Type-Recovery
+      rekonstruiert C-Structures und Function-Signatures automatisch durch
+      Data-Flow-Analysis. Plugin-API (Python/C++) ermöglicht tiefe Customization
+      und Integration in Custom-Workflows. Cloud-Collaboration synchronisiert
+      Binary-Analysis zwischen verteilten Teams in Real-Time. Modern-GUI mit
+      Multiple-Workspaces, Split-Views und Customizable-Layouts übertrifft
+      Legacy-Tools in Usability und Workflow-Efficiency. Debugger-Integration
+      für Dynamic-Analysis-Correlation.
     url: https://binary.ninja
-    icon: 🛠️
+    skillLevel: advanced
     domains:
       - malware-analysis
       - static-investigations
     phases:
       - analysis
-    tags:
-      - reverse-engineering
-      - decompiler
-    related_concepts: null
     platforms:
       - Windows
       - macOS
       - Linux
-    accessType: commercial
+    tags:
+      - reverse-engineering
+      - decompiler
+    icon: 🛠️
     license: Proprietary
-    knowledgebase: false
+    accessType: commercial
   - name: CapLoader
     type: software
     description: >-
@@ -3340,7 +3070,6 @@ tools:
       - credential-extraction
       - file-reconstruction
       - batch-processing
-    related_concepts: null
     platforms:
       - Windows
     accessType: commercial
@@ -3349,29 +3078,42 @@ tools:
   - name: Collabora Online
     type: software
     description: >-
-      Web‑basierte Open‑Source‑Office‑Suite mit kompletter
-      Dokumenten‑Bearbeitung und Live‑Kollaboration.
-    skillLevel: beginner
+      Ermöglicht sichere Echtzeit-Dokumentenbearbeitung für verteilte
+      Ermittlungsteams ohne Cloud-Abhängigkeit. Erstellt gerichtsfeste Berichte
+      mit vollständiger Versionskontrolle und Änderungshistorie. Unterstützt
+      alle gängigen Office-Formate, integriert nahtlos in
+      Nextcloud-Infrastrukturen. Verschlüsselte Kommunikation und granulare
+      Berechtigungen schützen sensible Ermittlungsdaten. Offline-Fähigkeiten für
+      abgeschottete Laborumgebungen.
     url: https://www.collaboraonline.com
-    icon: 📝
+    skillLevel: beginner
     domains:
       - collaboration-general
+    domain-agnostic-software:
+      - collaboration-general
     phases:
       - reporting
-    tags:
-      - office
-      - collaboration
     platforms:
       - Linux
       - Windows
+    tags:
+      - office
+      - collaboration
+    icon: 📝
+    license: MPL-2.0 OR AGPL-3.0
     accessType: download
-    license: "MPL\_/ AGPL"
-    knowledgebase: false
   - name: ShadowExplorer
-    icon: 🗂️
     type: software
     description: >-
-      Das schlanke Windows-Tool macht Volume-Shadow-Copy-Snapshots auch in Home-Editionen sichtbar und erlaubt das komfortable Durchstöbern sowie Wiederherstellen früherer Datei-Versionen. Damit lassen sich versehentlich gelöschte oder überschriebene Dateien in Sekunden zurückholen – geeignet für schnelle Triage und klassische Datenträgerforensik.
+      Macht Windows Volume Shadow Copy Snapshots auch in Home-Editionen für
+      forensische Analyse zugänglich. Ermöglicht komfortables Durchstöbern und
+      Wiederherstellen früherer Dateiversionen ohne komplexe
+      Kommandozeilen-Tools. Rekonstruiert gelöschte oder überschriebene Dateien
+      aus automatischen Systemsicherungen. Besonders wertvoll für
+      Zeitpunkt-basierte Analysen und Nachweis von Datenmanipulationen. Schlanke
+      GUI für schnelle Triage klassischer Datenträgerforensik.
+    url: https://www.shadowexplorer.com/
+    skillLevel: novice
     domains:
       - static-investigations
       - incident-response
@@ -3380,15 +3122,6 @@ tools:
       - analysis
     platforms:
       - Windows
-    related_software:
-      - OSFMount
-      - PhotoRec
-    domain-agnostic-software: null
-    skillLevel: novice
-    accessType: download
-    url: https://www.shadowexplorer.com/
-    license: Freeware
-    knowledgebase: false
     tags:
       - gui
       - shadow-copy
@@ -3399,24 +3132,24 @@ tools:
       - point-in-time-restore
     related_concepts:
       - Digital Evidence Chain of Custody
-
-
+    related_software:
+      - OSFMount
+      - PhotoRec
+    icon: 🗂️
+    license: Freeware
+    accessType: download
   - name: Sonic Visualiser
-    icon: 🎵
     type: software
     description: >-
-      Die Open-Source-Audio-Analyse-Suite wird in der Forensik eingesetzt,
-      um Wave- und Kompressionsformate bis auf Sample-Ebene zu untersuchen.
-      Spektrogramm-Visualisierung, Zeit-/Frequenz-Annotationen und
-      Transkriptions-Plugins (Vamp) helfen, Manipulationen wie
-      Bandpass-Filter, Time-Stretching oder Insert-Edits nachzuweisen.
-      FFT- und Mel-Spectral-Views decken versteckte Audio-Watermarks oder
-      Steganografie auf. Export-Funktionen in CSV/JSON erlauben die
-      Weiterverarbeitung in Python-Notebooks oder SIEM-Pipelines.
-      Ideal für Voice-Authentication-Checks, Deep-Fake-Erkennung
-      und Beweisaufbereitung vor Gericht.
-    skillLevel: intermediate
+      Analysiert Audio-Dateien bis auf Sample-Ebene für Authentizitätsprüfung
+      und Manipulationserkennung in forensischen Ermittlungen.
+      Spektrogramm-Visualisierung und FFT-basierte Analysen decken versteckte
+      Bearbeitungen auf. Vamp-Plugin-Ökosystem erweitert Analysefähigkeiten um
+      Beat-Detection und Pitch-Tracking. Annotation-Ebenen dokumentieren
+      Findings mit präzisen Zeitstempeln für Gerichtspräsentationen.
+      Export-Funktionen integrieren Ergebnisse in forensische Analyse-Pipelines.
     url: https://www.sonicvisualiser.org/
+    skillLevel: intermediate
     domains:
       - static-investigations
       - fraud-investigation
@@ -3428,9 +3161,6 @@ tools:
       - Windows
       - Linux
       - macOS
-    accessType: download
-    license: GPL-2.0
-    knowledgebase: false
     tags:
       - gui
       - audio-forensics
@@ -3438,25 +3168,24 @@ tools:
       - plugin-support
       - annotation
       - csv-export
-    related_concepts: []
-    related_software:
-      - Audacity
-
+    icon: 🎵
+    license: GPL-2.0
+    accessType: download
   - name: Dissect
-    icon: 🧩
     type: software
     description: >-
-      Fox-ITs Python-Framework abstrahiert Windows- und Linux-Speicherabbilder
-      in virtuelle Objekte (Prozesse, Dateien, Registry, Kernel-Strukturen),
-      ohne zuvor ein Profil definieren zu müssen. Modularer
-      Hypervisor-Layer erlaubt das Mounten und gleichzeitige Analysieren
-      mehrerer Memory-Dumps – perfekt für großflächige Incident-Response.
-      Plugins dekodieren PTEs, handle tables, APC-Queues und liefern
-      YARA-kompatible Scans. Die Zero-Copy-Architektur beschleunigt Queries auf
-      Multi-GB-Images signifikant. Unterstützt Windows 11 24H2-Kernel sowie
-      Linux 6.x-schichten ab Juli 2025.
-    skillLevel: advanced
+      Python-Framework abstrahiert Windows- und Linux-Speicherabbilder in
+      virtuelle Dateisystem-Objekte ohne vorherige Profil-Definition oder
+      OS-spezifische Konfiguration. Modularer Hypervisor-Layer ermöglicht
+      simultane Multi-Image-Analyse für großflächige Incident-Response-Scenarios
+      mit einheitlicher API. Zero-Copy-Architektur beschleunigt Memory-Queries
+      auf Multi-GB-Images durch direkten Memory-Mapping ohne Intermediate-Files.
+      Plugin-System dekodiert Windows-Strukturen: PTEs, Handle-Tables,
+      APC-Queues und Kernel-Objects automatisch. Besonders effizient bei
+      Batch-Processing mehrerer Memory-Dumps parallel durch
+      Threading-Optimierung und Resource-Sharing zwischen Analyse-Instanzen.
     url: https://github.com/fox-it/dissect
+    skillLevel: advanced
     domains:
       - incident-response
       - malware-analysis
@@ -3468,11 +3197,8 @@ tools:
       - Windows
       - Linux
       - macOS
-    accessType: download
-    license: Apache 2.0
-    knowledgebase: false
     tags:
-      - command-line
+      - cli
       - memory-analysis
       - plugin-support
       - python-library
@@ -3483,18 +3209,20 @@ tools:
     related_software:
       - Volatility 3
       - Rekall
-
+    icon: 🧩
+    license: Apache-2.0
+    accessType: download
   - name: Docker Explorer
     icon: 🐳
     type: software
     description: >-
       Googles Forensik-Toolkit zerlegt Offline-Docker-Volumes und
       Overlay-Dateisysteme ohne laufenden Daemon. Es extrahiert
-      Container-Config, Image-Layer, ENV-Variablen, Mounted-Secrets
-      und schreibt Timeline-fähige Metadata-JSONs. Unterstützt btrfs,
-      overlay2 und zfs Storage-Driver sowie Docker Desktop (macOS/Windows).
-      Perfekt, um bösartige Images nach Supply-Chain-Attacken zu enttarnen
-      oder flüchtige Container nach einem Incident nachträglich zu analysieren.
+      Container-Config, Image-Layer, ENV-Variablen, Mounted-Secrets und schreibt
+      Timeline-fähige Metadata-JSONs. Unterstützt btrfs, overlay2 und zfs
+      Storage-Driver sowie Docker Desktop (macOS/Windows). Perfekt, um bösartige
+      Images nach Supply-Chain-Attacken zu enttarnen oder flüchtige Container
+      nach einem Incident nachträglich zu analysieren.
     skillLevel: intermediate
     url: https://github.com/google/docker-explorer
     domains:
@@ -3510,31 +3238,28 @@ tools:
       - macOS
       - Windows
     accessType: download
-    license: Apache 2.0
+    license: Apache-2.0
     knowledgebase: false
     tags:
-      - command-line
+      - cli
       - container-forensics
       - docker
       - timeline
       - json-export
       - supply-chain
-    related_concepts: []
     related_software:
       - Velociraptor
       - osquery
-
   - name: Ghiro
     icon: 🖼️
     type: software
     description: >-
       Die Web-basierte Bild­forensik-Plattform automatisiert EXIF-Analyse,
-      Hash-Matching, Error-Level-Evaluation (ELA) und
-      Steganografie-Erkennung für große Dateibatches. Unterstützt
-      Gesichts- und NSFW-Detection sowie GPS-Reverse-Geocoding für
-      Bewegungsprofile. Reports sind gerichtsfest
-      versioniert, REST-API und Celery-Worker skalieren auf
-      Millionen Bilder – ideal für CSAM-Ermittlungen oder Fake-News-Prüfung.
+      Hash-Matching, Error-Level-Evaluation (ELA) und Steganografie-Erkennung
+      für große Dateibatches. Unterstützt Gesichts- und NSFW-Detection sowie
+      GPS-Reverse-Geocoding für Bewegungsprofile. Reports sind gerichtsfest
+      versioniert, REST-API und Celery-Worker skalieren auf Millionen Bilder –
+      ideal für CSAM-Ermittlungen oder Fake-News-Prüfung.
     skillLevel: intermediate
     url: https://getghiro.org/
     domains:
@@ -3563,22 +3288,24 @@ tools:
     related_software:
       - ExifTool
       - PhotoRec
-
   - name: Sherloq
-    icon: 🔍
     type: software
     description: >-
-      Das Python-GUI-Toolkit für visuelle Datei-Analyse kombiniert
-      klassische Reverse-Steganografie-Techniken (LSB, Palette-Tweaking,
-      DCT-Coefficient-Scanning) mit modernen CV-Algorithmen.
-      Heatmaps und Histogramm-Diffs zeigen Manipulations-Hotspots,
-      während eine „Carve-All-Layers“-Funktion versteckte Daten in PNG,
-      JPEG, BMP, GIF und Audio-Spectra aufspürt. Plugins für zsteg,
-      binwalk und exiftool erweitern die Pipeline.
-      Eine Must-have-Ergänzung zu Ghidra & friends, wenn
-      Malware Dateien als Dead-Drop nutzt.
-    skillLevel: intermediate
+      Python-basiertes Image-Forensics-Toolkit kombiniert klassische
+      Steganography-Detection-Techniken mit modernen Computer-Vision-Algorithmen
+      für Manipulation-Analysis. LSB-Steganography-Detection, Palette-Analysis,
+      DCT-Coefficient-Examination und Statistical-Tests identifizieren
+      Hidden-Data in Images. Error-Level-Analysis (ELA) und
+      Noise-Pattern-Examination zeigen Compression-Artifacts und Edit-Traces in
+      JPEG-Files. Heatmap-Visualizations und Histogram-Differential-Analysis
+      markieren Manipulation-Hotspots automatisch.
+      Metadata-Inconsistency-Detection vergleicht EXIF-Data mit Image-Content
+      für Authenticity-Verification. Plugin-Architecture integriert externe
+      Tools: zsteg, binwalk, exiftool für Comprehensive-Image-Analysis-Pipeline.
+      Essential-Complement zu Hex-Editors und Reverse-Engineering-Tools für
+      Multimedia-Forensics.
     url: https://github.com/GuidoBartoli/sherloq
+    skillLevel: intermediate
     domains:
       - malware-analysis
       - static-investigations
@@ -3589,9 +3316,6 @@ tools:
       - Windows
       - Linux
       - macOS
-    accessType: download
-    license: MIT
-    knowledgebase: false
     tags:
       - gui
       - image-forensics
@@ -3604,76 +3328,61 @@ tools:
     related_software:
       - Ghiro
       - CyberChef
-
+    icon: 🔍
+    license: MIT
+    accessType: download
   - name: Cortex
     type: software
     description: >-
-      TheHives Analyzer-Engine automatisiert Observable-Intelligence durch über 
-      100 integrierte Services von VirusTotal bis Shodan. Ein File-Hash
-      triggert  parallel: AV-Scans, Sandbox-Detonation, YARA-Matching,
-      Reputation-Checks  in Sekunden statt manueller Stunden-Arbeit. Die
-      Plugin-Architektur erweitert  für Custom-APIs und interne
-      Threat-Intelligence. Besonders wertvoll:  Responder-Actions ermöglichen
-      automatische Incident-Response (Block-IP,  Quarantine-Host),
-      Taxonomy-Integration kategorisiert Threats nach MISP- Standards,
-      Job-History dokumentiert alle Analysen für Audit-Trails. 
-      Docker-Deployment skaliert Workers je nach Load. Rate-Limiting verhindert 
-      API-Quota-Erschöpfung. Die RESTful-API integriert in SOAR-Playbooks. 
-      JSON-Templates definieren Analyzer-Konfigurationen. Community-Analyzer 
-      erweitern für spezielle Use-Cases. Nach TheHive-Übernahme durch
-      StrangeBee  wird Cortex 3.x als Open-Source weiterentwickelt.
-      Standard-Component  moderner SOC-Automatisierung.
-    skillLevel: intermediate
+      Automatisiert Observable-Intelligence durch über 100 integrierte
+      Analysedienste von VirusTotal bis Shodan für beschleunigte Ermittlungen.
+      Ein File-Hash triggert parallel AV-Scans, Sandbox-Detonation und
+      YARA-Matching in Sekunden statt manueller Stundenarbeit. Responder-Actions
+      ermöglichen automatische Incident-Response wie IP-Blockierung oder
+      Host-Quarantäne. Plugin-Architektur erweitert für interne
+      Threat-Intelligence-Quellen. Docker-Deployment skaliert Worker je nach
+      Analyselast.
     url: https://strangebee.com/cortex
-    icon: 🧩
+    skillLevel: intermediate
     domains:
       - incident-response
       - malware-analysis
     phases:
       - analysis
+    platforms:
+      - Linux
     tags:
       - automation
       - threat-intel
-    platforms:
-      - Linux
+    icon: 🧩
+    license: AGPL v3
     accessType: download
-    license: AGPL v3
-    knowledgebase: false
   - name: Elasticsearch
     type: software
     description: >-
-      Die verteilte Such- und Analytics-Engine bildet das Herzstück moderner 
-      Forensik-Infrastrukturen durch Near-Real-Time-Indexierung von Petabytes. 
-      Lucene-basierte Volltext-Suche findet IOCs in Sekunden über Millionen
-      Logs.  Die JSON-native Architektur verarbeitet strukturierte und
-      unstrukturierte  Daten gleichermäßen. Besonders wertvoll:
-      Aggregations-Framework erstellt  komplexe Timeline-Analysen, Geo-Queries
-      korrelieren Events geografisch,  Machine-Learning-Features erkennen
-      Anomalien automatisch. Horizontal- Skalierung von Single-Node bis
-      Multi-Datacenter-Clusters. Die RESTful-API  integriert mit allen
-      Forensik-Tools. Ingest-Pipelines normalisieren  verschiedene Log-Formate.
-      Security-Features (X-Pack) bieten Encryption und  RBAC für sensitive
-      Ermittlungen. Snapshot-Funktionen sichern forensische  Integrität. Die
-      Index-Lifecycle-Management rotiert alte Daten automatisch.  Basis für
-      ELK-Stack (Logstash, Kibana) und SIEM-Systeme. Unverzichtbar für  moderne
-      SOCs und Incident-Response-Teams.
-    skillLevel: intermediate
+      Durchsucht Petabytes forensischer Logs in Echtzeit durch verteilte
+      Lucene-basierte Volltextsuche für moderne SOC-Infrastrukturen. JSON-native
+      Architektur verarbeitet strukturierte und unstrukturierte Ermittlungsdaten
+      gleichzeitig. Aggregations-Framework erstellt komplexe Timeline-Analysen,
+      Geo-Queries korrelieren Events geografisch. Machine-Learning-Features
+      erkennen Anomalien automatisch. Horizontal skalierbar von Single-Node bis
+      Multi-Datacenter-Clustern. Basis für ELK-Stack und SIEM-Systeme.
     url: https://www.elastic.co/elasticsearch
-    icon: 🔍
+    skillLevel: intermediate
     domains:
       - incident-response
       - network-forensics
     phases:
       - analysis
-    tags:
-      - search
-      - big-data
     platforms:
       - Linux
       - Windows
+    tags:
+      - search
+      - big-data
+    icon: 🔍
+    license: Elastic-2.0 OR SSPL-1.0
     accessType: download
-    license: "Elastic\_License\_/\_SSPL"
-    knowledgebase: false
   - name: Elliptic
     type: software
     description: >-
@@ -3715,21 +3424,10 @@ tools:
   - name: FACT
     type: software
     description: >-
-      Das Firmware Analysis and Comparison Tool des BSI revolutioniert
-      IoT-Security  durch automatisierte Bulk-Analysis von Embedded-Firmware.
-      Web-Interface  ermöglicht Upload ganzer Firmware-Sammlungen für parallele
-      Verarbeitung.  Über 50 Analyzer dekomprimieren Archive, extrahieren
-      Dateisysteme,  identifizieren Crypto-Keys, finden Backdoors. Besonders
-      mächtig:  Diff-Analysis vergleicht Firmware-Versionen und identifiziert
-      Security- Patches, CVE-Matching findet bekannte Vulnerabilities in
-      embedded Libraries,  Entropy-Analysis lokalisiert verschlüsselte Bereiche.
-      Die Plugin-Architektur  ermöglicht Custom-Analyzer für proprietäre
-      Formate. YARA-Integration für  Malware-Detection. Collaboration-Features
-      für Team-Analysis. REST-API  automatisiert Bulk-Submissions. Die
-      PostgreSQL-Backend speichert Metadaten  durchsuchbar. Perfekt für
-      Router-Hersteller, Security-Researcher und  Incident-Response-Teams die
-      IoT-Compromises untersuchen. Open-Source  aber Enterprise-Ready für
-      kritische Infrastruktur-Assessments.
+      BSI-Framework automatisiert IoT-Firmware-Bulk-Analyse durch intelligente
+      Extraktion von Dateisystemen, Crypto-Keys und Backdoor-Detection.
+      Vergleicht Firmware-Versionen, identifiziert CVE-Matches und erstellt
+      Vulnerability-Reports für Router, Smart-Devices und Embedded-Systems.
     skillLevel: advanced
     url: https://github.com/fkie-cad/FACT_core
     icon: 🔧
@@ -3744,26 +3442,16 @@ tools:
     platforms:
       - Linux
     accessType: download
-    license: GPL v3
+    license: GPL v3
     knowledgebase: false
   - name: FOCA
     type: software
     description: >-
-      Elevenpaths (Telefonica) entwickelte dieses OSINT-Kraftpaket zur
-      Extraktion  von Metadaten aus öffentlich verfügbaren Dokumenten für
-      Infrastructure- Reconnaissance. Crawlt systematisch Websites nach PDFs,
-      Office-Docs,  Bildern und extrahiert: Autor-Namen (potentielle
-      Mitarbeiter), Software- Versionen (Attack-Surface), interne Pfade
-      (Network-Topology), Drucker- Namen (Physical-Access-Points). Die
-      DNS-Analysis korreliert Domains mit  gefundenen Infrastrukturdaten.
-      Geolocation-Features mappen IP-Ranges.  Besonders wertvoll für
-      Social-Engineering-Vorbereitung: Email-Pattern- Recognition generiert
-      Username-Listen, Organization-Chart-Reconstruction  aus Metadaten.
-      Plugin-System erweitert für Custom-Document-Types.  Shodan-Integration
-      enrichert IP-Intelligence. Die GUI macht komplexe  OSINT-Workflows auch
-      für Non-Technical-Investigators zugänglich.  Export-Funktionen für weitere
-      Analysis-Tools. Unverzichtbar für  Penetration-Tester und
-      Financial-Crime-Investigators die Target- Organizations verstehen müssen.
+      OSINT-Tool extrahiert Metadaten aus öffentlichen Dokumenten für
+      Infrastructure-Reconnaissance. Sammelt Mitarbeiter-Namen,
+      Software-Versionen, interne Netzwerkpfade und erstellt Angriffsvektoren
+      aus PDF/Office-Dateien. Perfekt für Social-Engineering-Vorbereitung und
+      Target-Profiling.
     skillLevel: beginner
     url: https://github.com/ElevenPaths/FOCA
     icon: 🗂️
@@ -3778,7 +3466,7 @@ tools:
     platforms:
       - Windows
     accessType: download
-    license: GPL v3
+    license: GPL v3
     knowledgebase: false
   - name: Firmware Analysis Toolkit
     type: software
@@ -3817,77 +3505,67 @@ tools:
   - name: GCHQ Tools
     type: software
     description: >-
-      Sammlung freier GCHQ‑Utilities, allen voran CyberChef\_– das
-      „Cyber‑Schweizer‑Taschenmesser“ für Encoding, Crypto und Datenanalyse.
-    skillLevel: beginner
+      Sammlung forensischer Werkzeuge der britischen GCHQ mit CyberChef als
+      Kernstück für Datenmanipulation und Dekodierung. Entschlüsselt Base64,
+      XOR-Verschlüsselung und andere Obfuskierungstechniken, extrahiert
+      versteckte Strings aus Binärdateien. Analysiert Entropie-Muster für
+      Verschlüsselungserkennung. Browser-basierte Bedienung ohne Installation,
+      Rezept-System für komplexe Verarbeitungsketten. Unverzichtbar für
+      Malware-Deobfuskation und schnelle Triage verdächtiger Artefakte.
     url: https://gchq.github.io/CyberChef
-    icon: 🥄
+    skillLevel: beginner
     domains:
       - domain-agnostic-software
     phases:
       - analysis
+    platforms:
+      - Web
     tags:
       - encoding
       - crypto
       - parsing
-    platforms:
-      - Web
+    icon: 🥄
+    license: Apache 2.0
     accessType: download
-    license: Apache 2.0
-    knowledgebase: false
   - name: Gephi
     type: software
     description: >-
-      Die Open-Source-Netzwerkanalyseplattform visualisiert komplexe 
-      Beziehungsgeflechte durch mächtige Graph-Algorithmen und interaktive 
-      Darstellung. Force-Atlas-Layout organisiert tausende Nodes automatisch, 
-      Modularity-Clustering identifiziert Communities, Betweenness-Centrality 
-      findet Schlüsselfiguren in Netzwerken. Besonders wertvoll für Fraud-
-      Investigations: Import von CSV/GEXF-Daten aus Transaktions-Logs, 
-      Timeline-Animation zeigt Netzwerk-Evolution, Size/Color-Mapping  nach
-      Attributen (Geldbeträge, Risikolevels). Die Statistics-Panel  berechnet
-      Graph-Metriken automatisch. Filter isolieren verdächtige  Subgraphen.
-      Export zu PDF/SVG für Reports. Plugin-Ecosystem erweitert  für spezielle
-      Analysis-Methoden. Besonders stark bei großen Datensätzen  (100k+ Nodes)
-      wo andere Tools versagen. Die Java-Basis macht es  Cross-Platform
-      verfügbar. Steile Lernkurve aber unübertroffene  Visualisierungs-Power für
-      Social-Network-Analysis und  Money-Laundering-Detection.
-    skillLevel: intermediate
+      Open-Source Graph-Visualization und Network-Analysis-Tool spezialisiert
+      auf Large-Scale-Dataset-Processing durch Force-Atlas-Layout-Algorithmen
+      und Community-Detection-Methods. Importiert Financial-Transaction-Data aus
+      CSV/GEXF-Formats, identifiziert Money-Laundering-Patterns,
+      Shell-Company-Networks und Fraud-Rings durch Statistical-Network-Analysis.
+      Modularity-Algorithmen segmentieren große Netzwerke automatisch in
+      Communities, Betweenness-Centrality identifiziert Key-Players und
+      Bottlenecks. Besonders effektiv bei Datasets mit 100k+ Nodes für
+      Financial-Crime-Investigations und Social-Network-Analysis.
+      Dynamic-Network-Analysis für Time-Series-Data, Export-Capabilities für
+      Report-Generation und Courtroom-Presentations. Plugin-Ecosystem erweitert
+      Analysis-Capabilities für Domain-Specific-Use-Cases.
     url: https://gephi.org
-    icon: 🕸️
+    skillLevel: intermediate
     domains:
       - fraud-investigation
       - network-forensics
     phases:
       - analysis
-    tags:
-      - graph-analysis
-      - visualization
     platforms:
       - Windows
       - macOS
       - Linux
+    tags:
+      - graph-analysis
+      - visualization
+    icon: 🕸️
+    license: GPL-3.0 OR CDDL-1.0
     accessType: download
-    license: "GPL\_v3\_/\_CDDL"
-    knowledgebase: false
   - name: Google Earth Pro
     type: software
     description: >-
-      Googles professionelle Geo-Intelligence-Plattform bietet forensische 
-      Capabilities weit jenseits der Consumer-Version. Historische
-      Satellitenbilder  ab 1984 ermöglichen Timeline-Analysis von Tatorten,
-      Gebäude-Entwicklungen,  Umwelt-Veränderungen. High-Resolution-Imagery (bis
-      60cm/Pixel) zeigt  Details für Crime-Scene-Reconstruction. Besonders
-      wertvoll: KML/KMZ-Import  von GPS-Tracks für Movement-Analysis,
-      Measurement-Tools für genaue  Distanzen, 3D-Buildings für
-      Sichtlinien-Analysen. Movie-Maker erstellt  Flyover-Animations für
-      Jury-Präsentationen. The Street-View-Integration  zeigt
-      Ground-Truth-Perspective. Polygon-Tools markieren Search-Areas. 
-      Koordinaten-Anzeige in verschiedenen Formaten (Lat/Long, UTM, MGRS). 
-      Offline-Caching für Field-Operations. Die kostenlose Professional-Lizenz 
-      (seit 2015) demokratisiert Geo-Intelligence. GPS-Data-Import von 
-      Smartphones, Fitness-Trackern, Fahrzeug-Systemen. Standard-Tool für 
-      OSINT-Verification und Court-Room-Presentations weltweit.
+      Forensische Geo-Intelligence-Plattform mit historischen Satellitenbildern
+      ab 1984 für Timeline-Analysen. Visualisiert GPS-Tracks, misst
+      Tatort-Distanzen präzise und erstellt 3D-Rekonstruktionen. KML-Import
+      ermöglicht Movement-Pattern-Analysis für Alibis und Bewegungsprofile.
     skillLevel: beginner
     url: https://www.google.com/earth/versions/#earth-pro
     icon: 🌐
@@ -3943,102 +3621,62 @@ tools:
   - name: IDA Pro
     type: software
     description: >-
-      Vertreten im Bereich des Reverse Engineering seit über 30 Jahren - 
-      HexRays IDA Pro definierte die statische Binary-Analyse. Der  Disassembler
-      unterstützt über 50 Prozessor-Architekturen von x86  über ARM bis zu
-      exotischen DSPs. Der Decompiler wandelt  Assembly in lesbaren C-Pseudocode
-      um, interaktive Cross-References  visualisieren Code-Beziehungen, der
-      Graph-View zeigt Control-Flow intuitiv.  IDAPython ermöglicht
-      Automatisierung komplexer Analysen. Die Signature- Datenbank (FLIRT)
-      identifiziert Standard-Bibliotheken automatisch.  Collaborative-Features
-      für Teamanalysen. Besonders stark bei Malware- Dekonstruktion,
-      Vulnerability-Research und Firmware-Analyse. Der Preis  schreckt
-      Hobbyisten ab. 
-    skillLevel: advanced
+      Industry-Standard Reverse-Engineering-Platform seit über 30 Jahren mit
+      Disassembler für 50+ Prozessor-Architekturen von x86/x64 über ARM, MIPS
+      bis zu exotischen DSPs und Custom-Silicon. Hex-Rays Decompiler wandelt
+      Assembly-Code in lesbaren C-Pseudocode um mit Variable-Recovery und
+      Type-Reconstruction. Interactive Cross-References visualisieren
+      Code-Beziehungen und Call-Graphs für komplexe Binary-Analysis.
+      FLIRT-Signature-Datenbank identifiziert Standard-Bibliotheken und
+      Known-Functions automatisch für Noise-Reduction. IDAPython ermöglicht
+      tiefgreifende Automatisierung komplexer Analysen durch vollständige
+      API-Access. Collaborative-Features für Multi-Analyst-Teams, besonders
+      stark bei Advanced-Malware-Dekonstruktion und
+      Zero-Day-Vulnerability-Research.
     url: https://hex-rays.com/ida-pro
-    icon: 🖥️
+    skillLevel: advanced
     domains:
       - malware-analysis
       - static-investigations
     phases:
       - analysis
-    tags:
-      - disassembler
-      - decompiler
     platforms:
       - Windows
       - macOS
       - Linux
-    accessType: commercial
-    license: Proprietary
-    knowledgebase: false
-  - name: KAPE
-    type: software
-    description: >-
-      Kroll Artifact Parser and Extractor versucht sich an Windows-Forensik durch 
-      intelligente Ziel-basierte Sammlung. Statt Full-Disk-Images extrahiert
-      KAPE  gezielt kritische Artefakte: Registry-Hives, Event-Logs, Prefetch,
-      Browser- Daten, Scheduled-Tasks in Minuten statt Stunden. Die Target-Files
-      definieren  was gesammelt wird, Module-Files wie es verarbeitet wird.
-      Besonders clever:  Compound-Targets gruppieren zusammengehörige Artefakte
-      (z.B. "Browser" sammelt  Chrome+Firefox+Edge), die gKAPE-GUI macht es auch
-      für Nicht-Techniker  zugänglich. Batch-Mode verarbeitet mehrere Images
-      parallel. Output direkt  kompatibel zu Timeline-Tools wie Plaso. 
-      VSS-Processing analysiert Shadow- Copies automatisch. Der
-      Remote-Collection-Mode sammelt über Netzwerk.  Kostenlos (mit Registrierung) aber
-      Enterprise-Support verfügbar.
-    skillLevel: intermediate
-    url: https://www.kroll.com/kape
-    icon: 🧰
-    domains:
-      - incident-response
-      - static-investigations
-    phases:
-      - data-collection
     tags:
-      - artifact-collection
-      - triage
-    platforms:
-      - Windows
-    accessType: download
+      - disassembler
+      - decompiler
+    icon: 🖥️
     license: Proprietary
-    knowledgebase: false
+    accessType: commercial
   - name: Kibana
     type: software
     description: >-
-      Das Visualisierungs-Frontend des Elastic-Stacks verwandelt rohe
-      Forensik-Daten  in aussagekräftige Dashboards und Echtzeit-Analysen.
-      Drag-and-Drop-Interface  erstellt komplexe Queries ohne
-      Programmierkenntnisse. Besonders wertvoll für  Incident-Response:
-      Timeline-Visualisierungen korrelieren Events über  verschiedene Systeme,
-      Geo-Maps zeigen Angriffs-Ursprünge, Heat-Maps  identifizieren
-      Aktivitätsmuster. Die Discover-Funktion ermöglicht explorative 
-      Datenanalyse mit Drill-Down-Capabilities. Canvas erstellt Infografiken
-      für  Management-Reports. Machine-Learning-Integration erkennt Anomalien
-      automatisch.  Alerting-Framework triggert bei verdächtigen Mustern.
-      Dashboard-Sharing für  SOC-Teams. Der Dev-Tools-Bereich bietet direkten
-      Elasticsearch-Zugriff für  Power-User. Spaces isolieren verschiedene
-      Ermittlungen. Die Integration mit  Wazuh, Suricata und anderen
-      SIEM-Komponenten macht es zum Standard-Dashboard  moderner
-      Security-Operations. Kostenlos bis zu Basic-Features,  X-Pack-Lizenzierung
-      für Enterprise-Funktionen.
-    skillLevel: beginner
+      Verwandelt forensische Rohdaten in aussagekräftige Dashboards und
+      Echtzeit-Analysen für Incident-Response-Teams. Timeline-Visualisierungen
+      korrelieren Events über verschiedene Systeme, Geo-Maps zeigen
+      Angriffs-Ursprünge. Heat-Maps identifizieren Aktivitätsmuster automatisch.
+      Drag-and-Drop-Interface erstellt komplexe Queries ohne
+      Programmierkenntnisse. Canvas erstellt Infografiken für
+      Management-Reports, Machine-Learning-Integration erkennt Anomalien.
+      Dashboard-Sharing für SOC-Teams.
     url: https://www.elastic.co/kibana
-    icon: 📊
+    skillLevel: beginner
     domains:
       - incident-response
     phases:
       - analysis
       - reporting
-    tags:
-      - dashboards
-      - analytics
     platforms:
       - Linux
       - Windows
-    accessType: download
+    tags:
+      - dashboards
+      - analytics
+    icon: 📊
     license: "Elastic\_License\_/\_SSPL"
-    knowledgebase: false
+    accessType: download
   - name: LiME
     type: software
     description: >-
@@ -4067,11 +3705,14 @@ tools:
       - memory
       - acquisition
       - scenario:memory_dump
+      - memory-analysis
+      - ram-acquisition
+      - kernel-module
     platforms:
       - Linux
       - Android
     accessType: download
-    license: GPL v2
+    license: GPL v2
     knowledgebase: false
   - name: Loki
     type: software
@@ -4105,98 +3746,75 @@ tools:
       - Windows
       - Linux
     accessType: download
-    license: GPL v3
+    license: GPL v3
     knowledgebase: false
   - name: Maltego
     type: software
     description: >-
-      Die Link-Analysis-Suite transformiert OSINT-Recherchen in visuelle
-      Netzwerke  für Fraud-Ermittlungen und Threat-Intelligence. Graph-basierte
-      Darstellung  zeigt Verbindungen zwischen Personen, Domains, IP-Adressen,
-      Social-Media- Accounts intuitiv. Transform-Engine automatisiert
-      Datensammlung aus hunderten  Quellen: DNS-Records, Whois-Daten,
-      Social-Networks, Darkweb-Mentions.  Besonders mächtig für BEC-Fraud:
-      Email-to-Domain-to-Infrastructure-Mapping  enthüllt Scammer-Netzwerke. Die
-      Maltego-Teeth-Integration bringt kommerzielle  Datenquellen.
-      Machine-Learning-Algorithmen finden versteckte Cluster. 
-      Collaboration-Features für Team-Ermittlungen. Case-Management
-      dokumentiert  Recherche-Pfade. Export zu verschiedenen Formaten für
-      Reports. Die  Community-Edition (kostenlos) limitiert auf 12 Entities,
-      aber ausreichend  für kleinere Fälle. Professional-Versionen (ab 999€)
-      entfernen Limits.  Standard-Tool für Financial-Crime-Units und
-      OSINT-Analysten weltweit.
-    skillLevel: intermediate
+      OSINT-Link-Analysis-Platform transformiert manuelle Recherchen in
+      automatisierte visuelle Netzwerk-Investigations durch umfangreiche
+      Transform-Engine-Ecosystem. Korreliert automatisch Domains, IP-Adressen,
+      Email-Addresses, Social-Media-Accounts und Phone-Numbers für
+      Attribution-Analysis. Machine-Learning-Enhanced-Clustering identifiziert
+      BEC-Fraud-Networks, Botnet-Infrastructure und
+      Multi-Stage-Attack-Campaigns. Transform-Hub erweitert Datenquellen um 100+
+      Commercial- und Open-Source-Intelligence-Feeds. Maltego CE bietet
+      Community-Edition mit Basic-Transforms kostenlos, Commercial-Versions
+      integrieren Premium-Data-Sources. Automated-Reconnaissance-Workflows für
+      Threat-Actor-Profiling und Infrastructure-Mapping reduzieren manuelle
+      Investigation-Time erheblich.
     url: https://www.maltego.com
-    icon: 🌐
+    skillLevel: intermediate
     domains:
       - fraud-investigation
       - network-forensics
     phases:
       - analysis
+    platforms:
+      - Windows
+      - macOS
+      - Linux
     tags:
       - osint
       - link-analysis
-    platforms:
-      - Windows
-      - macOS
-      - Linux
-    accessType: freemium
+    icon: 🌐
     license: Proprietary
-    knowledgebase: false
   - name: OnlyOffice
     type: software
     description: >-
-      Die kollaborationsfokussierte Office-Suite kombiniert Microsoft-Office-
-      Kompatibilität mit Open-Source-Flexibilität für forensische Team-Arbeit. 
-      Real-Time-Co-Editing ermöglicht simultane Report-Erstellung durch mehrere 
-      Ermittler. Besonders wertvoll: Version-History dokumentiert alle
-      Änderungen  für Audit-Trails, Comment-System koordiniert Review-Prozesse, 
-      PDF-Forms für strukturierte Evidence-Collection. Die Plugin-Architecture 
-      integriert mit Nextcloud, ownCloud für sichere File-Sharing. 
-      Macro-Support (mit Sicherheits-Sandboxing) automatisiert repetitive 
-      Analysen. Advanced-Mail-Merge für Bulk-Correspondence. Mathematical-
-      Formulas in Spreadsheets für Statistical-Analysis. Die Self-Hosted-
-      Deployment-Option hält sensitive Ermittlungsdaten in-house. 
-      Enterprise-Features: SSO-Integration, LDAP-Authentication, Custom-
-      Branding für behördliche Anforderungen. Mobile-Apps ermöglichen 
-      Field-Documentation. Deutlich günstiger als Microsoft-Lizenzen bei 
-      vergleichbarer Funktionalität. Perfekte Balance zwischen Features  und
-      Datenschutz für professionelle Forensik-Teams.
-    skillLevel: beginner
+      Kollaborative Bürosuite mit vollständiger Microsoft-Kompatibilität für
+      forensische Berichtserstellung ohne Vendor-Lock-in. Echtzeit-Co-Editing
+      ermöglicht simultane Reporterstellung durch mehrere Ermittler,
+      Versionshistorie dokumentiert alle Änderungen für Audit-Trails.
+      SSO-Integration und granulare Berechtigungen sichern
+      Multi-User-Forensik-Workflows. Self-Hosting-Option für maximale
+      Datenkontrolle bei sensiblen Ermittlungen.
     url: https://www.onlyoffice.com
-    icon: 📄
+    skillLevel: beginner
     domains:
       - collaboration-general
+    domain-agnostic-software:
+      - collaboration-general
     phases:
       - reporting
-    tags:
-      - office
-      - collaboration
     platforms:
       - Windows
       - Linux
       - macOS
+    tags:
+      - office
+      - collaboration
+    icon: 📄
+    license: AGPL-3.0 OR Apache-2.0
     accessType: download
-    license: "AGPL\_/\_Apache\_2.0"
-    knowledgebase: false
   - name: OpenCTI
     type: software
     description: >-
-      Filigrans Cyber-Threat-Intelligence-Plattform orchestriert moderne 
-      Threat-Research durch Knowledge-Graph-basierte Datenmodellierung nach 
-      STIX-Standards. Import von IOCs, TTPs, Malware-Analysen aus hunderten 
-      Quellen: MISP-Feeds, Commercial-Intel, Custom-Research. Die Graph-Engine 
-      korreliert scheinbar unabhängige Indicators zu Attack-Campaigns. 
-      Besonders mächtig: Automated-Enrichment durch Connector-Ecosystem, 
-      Confidence-Scoring gewichtet Intel-Quality, Observable-Pivoting findet 
-      Related-Indicators automatisch. Playbook-Integration ermöglicht 
-      Automated-Response. Team-Collaboration durch Workspaces und 
-      Knowledge-Sharing. Export zu SIEM-Systemen in Real-Time. The 
-      Modern-React-UI macht komplexe Intelligence-Workflows intuitiv. 
-      Docker-Compose-Deployment für schnelle Installation. Enterprise- Features:
-      Multi-Tenancy, RBAC, Audit-Logging. Open-Source aber  Commercial-Support
-      verfügbar. Standard-Platform für moderne  CTI-Teams die structured
-      Intelligence-Sharing benötigen.
+      Cyber-Threat-Intelligence-Plattform orchestriert strukturierte IOC-Analyse
+      durch STIX-kompatible Knowledge-Graphs. Automatische Enrichment-Pipeline
+      korreliert scheinbar unabhängige Indicators zu Attack-Campaigns.
+      Confidence-Scoring gewichtet Intelligence-Qualität für präzise
+      Threat-Attribution.
     skillLevel: intermediate
     url: https://filigran.io/platforms/opencti
     icon: 🗂️
@@ -4210,191 +3828,156 @@ tools:
     platforms:
       - Linux
     accessType: download
-    license: AGPL v3
+    license: AGPL v3
     knowledgebase: false
   - name: Oxygen Forensic Suite
     type: software
     description: >-
-      Die umfassende Mobile-Forensik-Suite positioniert sich als Alternative  zu
-      westlichen Lösungen mit Fokus auf Android-Geräte und Cloud-Services. 
-      Besonders stark: Deep-Extraction chinesischer Smartphones (Xiaomi,
-      Huawei,  OnePlus), Telegram-Forensics inklusive Secret-Chats,
-      Signal-Database- Decryption bei Root-Access. Die Cloud-Explorer-Module
-      greifen auf 50+  Services zu: Google-Takeout, iCloud-Backups,
-      Microsoft-Accounts ohne  Premium-Pricing. Physical-Analyzer visualisiert
-      Timeline und Geo-Locations.  Die SQLite-Viewer parst App-Databases direkt.
-      Besonders innovativ:  UFED-Image-Import ermöglicht
-      Cross-Platform-Analysis, Live-Memory-Dumps  von Android-Geräten,
-      Malware-Detection in APKs. Der Preis (ab 8.000€)  unterbietet Marktführer
-      deutlich. Technisch solide Lösung mit  umfangreichem Cloud-Support und
-      regelmäßigen Updates für neue  Smartphone-Modelle und Apps.
-    skillLevel: advanced
+      Mobile-Forensik-Alternative mit Deep-Extraction-Capabilities für
+      Android-Systeme und spezialisierte Decryption-Engines für verschlüsselte
+      Messenger. Telegram-Secret-Chat-Dekryptierung durch proprietäre Methoden
+      und Live-Memory-Dumps von aktiven Geräten ohne Reboot-Requirement.
+      Analysiert über 50 Cloud-Services mit Direct-API-Access ohne
+      Premium-Pricing-Modell oder zusätzliche Lizenzkosten. Besonders stark bei
+      chinesischen Smartphones (Xiaomi, Huawei, OnePlus) mit proprietären
+      Security-Mechanismen und Custom-Android-Versionen.
+      Mobile-Triage-Capability für Vor-Ort-Screening, regelmäßige Updates für
+      neue App-Versionen und Android-Security-Patches innerhalb von 48 Stunden.
     url: https://www.oxygenforensics.com
-    icon: 📱
+    skillLevel: advanced
     domains:
       - mobile-forensics
     phases:
       - examination
       - analysis
+    platforms:
+      - Windows
     tags:
       - mobile
       - cloud
       - decryption
-    platforms:
-      - Windows
+    icon: 📱
     license: Proprietary
-    knowledgebase: false
   - name: Radare2
     type: software
     description: >-
-      Das modulare Reverse-Engineering-Framework vereint Disassembler,
-      Debugger,  Hex-Editor und Scripter in einer mächtigen CLI-Suite. Die
-      Unix-Philosophie  "do one thing well" ermöglicht granulare Kontrolle über
-      jeden Analyse-Schritt.  Besonders innovativ: r2pipe-Integration bindet das
-      Framework in Python,  Node.js, Go-Scripts ein, Visual-Mode bietet
-      Pseudo-GUI im Terminal,  Project-Files speichern komplexe Analysen
-      persistent. Über 20 Architekturen  unterstützt von x86 bis WebAssembly.
-      Die aktive Community entwickelt ständig  neue Features:
-      r2ghidra-Decompiler, r2dec für Pseudocode, cutter als GUI- Frontend.
-      Besonders stark bei Firmware-Analyse und CTF-Challenges. Die 
-      steep-Learning-Curve wird durch unübertroffene Flexibilität belohnt. 
-      Docker-Images vereinfachen Deployment. Die LGPL-Lizenz ermöglicht 
-      kommerzielle Integration. Perfekt für Sicherheitsforscher die maximale 
-      Kontrolle über ihre Analyse-Umgebung benötigen.
-    skillLevel: advanced
+      Open-Source modulares Reverse-Engineering-Framework vereint Disassembler,
+      Debugger, Hex-Editor und Binary-Analysis-Tools in mächtiger
+      Command-Line-Suite für Scriptable-Workflows. r2pipe-Integration bindet
+      Framework nahtlos in Python/Go/JavaScript-Scripts für
+      Automated-Analysis-Pipelines. Visual-Mode bietet interaktive Pseudo-GUI im
+      Terminal mit Navigation und Editing-Capabilities. Unterstützt 20+
+      CPU-Architekturen von x86 über ARM bis WebAssembly für Firmware-Analysis,
+      Embedded-Systems und CTF-Challenges. Scripting-Engine automatisiert
+      komplexe Multi-Step-Analysen, Plugin-System erweitert Funktionalität.
+      Active-Community-Development garantiert kontinuierliche Updates für neue
+      Architekturen und File-Formats. Besonders stark bei Embedded-Forensics und
+      IoT-Security-Analysis.
     url: https://rada.re/n/radare2.html
-    icon: 🗡️
+    skillLevel: advanced
     domains:
       - malware-analysis
     phases:
       - analysis
-    tags:
-      - reverse-engineering
-      - scripting
     platforms:
       - Windows
       - Linux
       - macOS
+    tags:
+      - reverse-engineering
+      - scripting
+    icon: 🗡️
+    license: LGPL v3
     accessType: download
-    license: LGPL v3
-    knowledgebase: false
   - name: Rekall
     type: software
     description: >-
-      Googles Memory-Analysis-Framework erweiterte Volatility um Cloud-Scale-
-      Capabilities und moderne Storage-Formate. AFF4-Integration ermöglicht 
-      Streaming-Analysis von Multi-Terabyte-Memory-Images ohne lokale Storage. 
-      Besonders innovativ: Live-Memory-Analysis über HTTP-Endpoints, 
-      Rekall-Agent für Remote-Collection, Timeline-Integration für 
-      Memory-Events. Python-3-Native-Architecture mit Jupyter-Notebook-
-      Integration für Interactive-Analysis. Web-UI demokratisiert Memory-
-      Forensics für Non-CLI-Users. Machine-Learning-Plugins für Anomaly-
-      Detection. Enterprise-Features: Distributed-Analysis, 
-      Multi-Investigator-Collaboration, Case-Management. Die Development  wurde
-      2018 eingestellt zugunsten anderer Google-Projekte, aber  Community-Forks
-      setzen Entwicklung fort. Legacy-Code läuft noch  stable für viele
-      Use-Cases. Historical-Significance als Volatility- Herausforderer und
-      Cloud-Memory-Forensics-Pioneer. Lessons-Learned  fließen in moderne Tools
-      wie Velociraptor ein.
-    skillLevel: advanced
+      Memory-Analysis-Framework mit Cloud-Scale-Capabilities und innovativer
+      Live-Memory-Analysis über HTTP-Endpoints für Remote-Forensics.
+      AFF4-Streaming-Support ermöglicht Untersuchung von Memory-Dumps ohne
+      lokale Storage durch On-Demand-Block-Loading. Jupyter-Notebook-Integration
+      schafft interaktive Speicher-Forensik-Umgebung für Collaboration zwischen
+      Analysten und Dokumentation. Web-UI demokratisiert Memory-Forensics für
+      nicht-technische Ermittler durch Point-and-Click-Interface. Besonders
+      stark bei verteilten Ermittlungen und Cloud-Infrastructure-Analysis mit
+      horizontaler Skalierung. Python-API ermöglicht Custom-Plugin-Development,
+      obwohl Google Development 2018 zugunsten anderer Projekte einstellte.
     url: https://github.com/google/rekall
-    icon: 🧬
+    skillLevel: advanced
     domains:
       - incident-response
     phases:
       - analysis
-    tags:
-      - memory
-      - python
     platforms:
       - Windows
       - Linux
       - macOS
+    tags:
+      - memory
+      - python
+    icon: 🧬
+    license: Apache 2.0
     accessType: download
-    license: Apache 2.0
-    knowledgebase: false
   - name: Suricata
     type: software
     description: >-
-      Die Open-Source-IDS/IPS-Engine der OISF revolutioniert Netzwerk-Security-
-      Monitoring durch Multi-Threading und moderne Protokoll-Decoder.
-      Lua-Scripts  ermöglichen Custom-Detection-Logic jenseits einfacher
-      Pattern-Matching.  Besonders mächtig: HTTP-Keyword-Matching in
-      Request-Bodies, TLS-Certificate- Fingerprinting, DNS-Tunneling-Detection,
-      File-Extraction aus Netzwerk- Streams. Die JSON-Ausgabe integriert nahtlos
-      in ELK-Stack oder Splunk.  Rule-Updates von Proofpoint, Emerging-Threats
-      halten Signaturen aktuell.  Bypass-Funktionen reduzieren False-Positives
-      bei bekanntem Traffic.  Hardware-Acceleration durch DPDK/PF_RING steigert
-      Performance auf 100Gbit+.  Cluster-Mode verteilt Load über mehrere
-      Instanzen. Die Engine bildet das  Herzstück kommerzieller SIEM-Systeme von
-      Elastic Security bis Wazuh.  Python-Output-Plugins ermöglichen
-      Custom-Integrations. Unverzichtbar für  moderne SOCs die leistungsstarke
-      Threat-Detection ohne Vendor-Lock-in  benötigen. Der De-facto-Standard für
-      Open-Source-NSM.
-    skillLevel: intermediate
+      Multi-Threading-IDS-Engine analysiert 100Gbit-Traffic durch
+      GPU-Acceleration und 
+        Hyperscan-Pattern-Matching für Real-Time-Threat-Detection. Lua-Scripts erstellen 
+        Custom-IOC-Detection-Logic, HTTP-Keyword-Matching extrahiert C2-Communication-Patterns. 
+        TLS-Certificate-Fingerprinting identifiziert Malware-Infrastructure, File-Extraction 
+        aus Network-Streams ermöglicht Payload-Analysis. JSON-Logs integrieren in ELK-Stack-Pipelines.
     url: https://suricata.io
-    icon: 🛡️
+    skillLevel: intermediate
     domains:
       - network-forensics
     phases:
       - analysis
-    tags:
-      - ids
-      - nsm
     platforms:
       - Linux
       - Windows
+    tags:
+      - ids
+      - nsm
+    icon: 🛡️
+    license: GPL v2
     accessType: download
-    license: GPL v2
-    knowledgebase: false
   - name: VirusTotal
     type: software
     description: >-
-      Googles Malware-Intelligence-Plattform aggregiert Erkennungen von 70+ 
-      Antivirus-Engines und Sandboxes für sofortige Threat-Assessment. Upload 
-      verdächtiger Dateien, URLs oder Domains liefert umfassende Reports: 
-      AV-Detections, Behavioral-Analysis, YARA-Matches, Community-Comments. 
-      Besonders wertvoll: Retro-Hunt findet ähnliche Samples in der 
-      Milliarden-Sample-Datenbank, Graph-View visualisiert Malware-Familien-
-      Beziehungen, Crowdsourced-IOCs von Security-Forschern weltweit. Die 
-      Premium-API ermöglicht Bulk-Searches und private Scans. Integration in 
-      alle Major-Security-Tools durch offene APIs. Livehunt-Service alertiert 
-      bei neuen Samples matching custom YARA-Rules. Vorsicht: Public-Uploads 
-      sind für alle sichtbar - keine vertraulichen Samples! Die kostenlose 
-      Nutzung macht es zum Standard-Tool für initiale Malware-Triage. 
-      Unverzichtbar für Threat-Intelligence und SOC-Analysten.
-    skillLevel: beginner
+      Google's Malware-Intelligence-Aggregation-Platform kombiniert 70+
+      Antivirus-Engine-Erkennungen für sofortige Multi-Vendor-Threat-Assessment
+      von Files, URLs, IP-Addresses und Domains. Retro-Hunt-Service durchsucht
+      historische Malware-Database mit YARA-Rules für ähnliche Samples durch
+      Fuzzy-Hashing und Behavioral-Signatures. Interactive-Graph-View
+      visualisiert Malware-Family-Relationships, C2-Infrastructure-Connections
+      und Campaign-Attribution durch Metadata-Correlation. Livehunt-Service
+      alertiert in Real-Time bei neuen YARA-Rule-Matches für proaktive
+      Threat-Hunting und IOC-Development. Community-Intelligence erweitert
+      Automated-Detection durch Analyst-Comments, Crowdsourced-Verdicts und
+      Threat-Actor-Attribution-Data für Enhanced-Context.
     url: https://www.virustotal.com
-    icon: 🦠
+    skillLevel: beginner
     domains:
       - malware-analysis
     phases:
       - examination
+    platforms:
+      - Web
     tags:
       - sandbox
       - av-scan
-    platforms:
-      - Web
-    accessType: cloud
+    icon: 🦠
     license: Freemium
     knowledgebase: true
   - name: WinHex
     type: software
     description: >-
-      X-Ways Software entwickelt seit 1995 den vielseitigsten Hex-Editor mit 
-      forensischen Superkräften. Über die reine Hex-Bearbeitung hinaus bietet 
-      WinHex: Disk-Cloning mit defekten Sektoren, RAM-Editor für Live-System-
-      Analyse, File-Recovery durch Signature-Scanning, Template-Engine für 
-      Datenstruktur-Parsing. Die Scripting-Engine automatisiert wiederkehrende 
-      Aufgaben. Besonders wertvoll: NTFS-Alternate-Data-Streams-Support, 
-      MFT-Browser zeigt Dateisystem-Metadaten, die Registry-Viewer parst Hives 
-      direkt. Integration mit forensischen Workstations über API. Der 
-      Specialist-License (499€) bietet erweiterte Forensik-Features, während 
-      die Standard-Version (38€) bereits beeindruckende Capabilities liefert. 
-      Die kompakte Exe-Datei (2MB) läuft ohne Installation. Besonders
-      geschätzt:  die präzise Byte-Level-Kontrolle die bei komplexen
-      Datenrettungen  entscheidend ist. Der Geheimtipp vieler Forensiker für
-      Spezialfälle.
+      Vielseitiger Hex-Editor mit forensischen Superkräften für Disk-Cloning,
+      RAM-Editing und File-Recovery durch Signature-Scanning.
+      NTFS-Alternate-Data-Streams-Support, MFT-Browser und Registry-Viewer
+      parsen Strukturen direkt. Template-Engine automatisiert
+      Datenstruktur-Parsing für komplexe Analysen.
     skillLevel: intermediate
     url: https://x-ways.net/winhex
     icon: 🗜️
@@ -4413,20 +3996,11 @@ tools:
   - name: WinPmem
     type: software
     description: >-
-      Velociraptors Windows-Memory-Imager revolutioniert RAM-Akquisition durch 
-      flexibles Driver-System und moderne WinAPI-Integration. Drei Acquisition-
-      Modi: Kernel-Driver für Maximum-Access, WinAPI für Modern-Windows, 
-      Live-Service für Remote-Collection. Die ELF64-Ausgabe enthält Metadaten 
-      für Volatility-Auto-Detection. Besonders clever: Pagefile-Integration 
-      erweitert verfügbaren Memory-Space, Registry-Acquisition parallel zum 
-      RAM-Dump, Hash-Verification für forensische Integrität. Driver-Signing 
-      für Secure-Boot-Kompatibilität. Die Portable-Version läuft ohne
-      Installation  von USB-Stick. JSON-Metadata dokumentiert
-      System-Configuration zum  Akquisitionszeitpunkt. Performance-Optimierung
-      für SSD-Storage.  Integration in KAPE-Workflows für automatisierte
-      Collection. Der  Open-Source-Ansatz ermöglicht Anpassungen für spezielle
-      Anforderungen.  Standard-Tool für Windows-Memory-Forensics wenn
-      kommerzielle Alternativen  nicht verfügbar oder zu teuer sind.
+      Velociraptor-Memory-Imager mit flexiblem Driver-System für
+      Windows-RAM-Akquisition. Drei Modi: Kernel-Driver für Maximum-Access,
+      WinAPI für Modern-Windows, Live-Service für Remote-Collection.
+      ELF64-Output mit Metadaten für Volatility-Auto-Detection,
+      Pagefile-Integration erweitert Memory-Space.
     skillLevel: advanced
     url: https://winpmem.velocidex.com
     icon: 💾
@@ -4438,96 +4012,71 @@ tools:
       - memory
       - acquisition
       - scenario:memory_dump
+      - memory-analysis
+      - ram-acquisition
+      - forensic-imaging
     platforms:
       - Windows
     accessType: download
-    license: GPL v2
+    license: GPL v2
     knowledgebase: false
   - name: Zeek
     type: software
     description: >-
-      Das programmierbare Netzwerk-Analysis-Framework (vormals Bro) geht weit 
-      über traditionelle Packet-Inspection hinaus - es interpretiert Protokolle 
-      und extrahiert strukturierte Logs für forensische Analyse. Die eingebaute 
-      Scripting-Sprache ermöglicht Custom-Protocol-Decoder und
-      Anomalie-Detection- Logic. Besonders wertvoll: Connection-Logs zeigen alle
-      Netzwerk-Sessions,  HTTP/DNS/TLS-Logs extrahieren Metadaten, File-Analysis
-      erkennt übertragene  Malware automatisch. Integration mit
-      Intelligence-Frameworks für IOC- Matching. Cluster-Deployment skaliert auf
-      Enterprise-Netzwerke. Die  Script-Community teilt Detectors für moderne
-      Threats: Cobalt-Strike- Beacons, DNS-Tunneling, Lateral-Movement-Patterns.
-      Real-Time-Streaming  zu SIEMs oder Offline-Analysis für Incident-Response.
-      Die Lernkurve ist  steil aber die Analysemächtigkeit unübertroffen. Basis
-      für kommerzielle  NSM-Lösungen und akademische Forschung. Standard in
-      Security-Teams die  Netzwerk-Forensik ernst nehmen.
-    skillLevel: advanced
+      Protocol-Analysis-Framework dekodiert Network-Traffic in strukturierte
+      Logs für 
+        forensische Timeline-Reconstruction: Connection-Tracking, HTTP-Transactions, DNS-Queries, 
+        TLS-Handshakes. Scripting-Language erstellt Custom-Protocol-Decoder und 
+        Behavioral-Anomaly-Detection, File-Analysis erkennt Malware-Transfers automatisch. 
+        Cluster-Deployment skaliert auf Multi-10Gbit-Links mit Load-Balancing.
     url: https://zeek.org
-    icon: 📈
+    skillLevel: advanced
     domains:
       - network-forensics
     phases:
       - analysis
-    tags:
-      - nsm
-      - scripting
     platforms:
       - Linux
       - macOS
-    accessType: download
+    tags:
+      - nsm
+      - scripting
+    icon: 📈
     license: BSD
-    knowledgebase: false
+    accessType: download
   - name: osquery
     type: software
     description: >-
-      Facebooks revolutionäre Endpoint-Telemetry-Engine transformiert
-      Betriebssystem- Zustand in SQL-Queries für forensische Untersuchungen. Das
-      "Operating System  as Database"-Konzept ermöglicht Abfragen wie "SELECT *
-      FROM processes WHERE  name LIKE '%malware%'". Über 200 Tables exposieren
-      System-Internals: Prozesse,  Netzwerkverbindungen, Dateien, Registry,
-      Scheduled-Tasks. Besonders mächtig  für Fleet-Wide-Hunting: eine Query
-      durchsucht tausende Endpoints parallel.  Die Event-Framework publiziert
-      Changes in Real-Time. Extensions erweitern  für Cloud-APIs, YARA-Scanning,
-      Custom-Tables. JSON-Output integriert nahtlos  in SIEMs.
-      Distributed-Queries über TLS-verschlüsselte Channels. Der 
-      Lightweight-Agent (< 50MB RAM) minimiert Performance-Impact.
-      Configuration- Management via JSON/YAML. Die Active-Community entwickelt
-      ständig neue  Tables. Basis für zahlreiche kommerzielle
-      Endpoint-Security-Produkte.  Unverzichtbar für moderne Threat-Hunting und
-      Incident-Response at Scale.
-    skillLevel: intermediate
+      SQL-basiertes Endpoint-Telemetry-System exposiert OS-State als querybare
+      Tables: 
+        laufende Prozesse, Registry-Keys, Scheduled-Tasks, Network-Connections. Fleet-Manager 
+        orchestriert Hunting-Queries über tausende Endpoints parallel, Event-Framework 
+        monitored Real-Time-Changes für Persistence-Detection. Custom-Tables erweitern 
+        für Application-Logs und Cloud-Instance-Metadata-Correlation.
     url: https://osquery.io
-    icon: 🗄️
+    skillLevel: intermediate
     domains:
       - incident-response
     phases:
       - examination
-    tags:
-      - endpoint
-      - sql
     platforms:
       - Linux
       - Windows
       - macOS
+    tags:
+      - endpoint
+      - sql
+    icon: 🗄️
+    license: Apache 2.0
     accessType: download
-    license: Apache 2.0
-    knowledgebase: false
   - name: tcpdump
     type: software
     description: >-
-      Der 1987 entwickelte Packet-Sniffer bleibt nach fast 40 Jahren das 
-      fundamentale Tool für Netzwerk-Troubleshooting und Traffic-Analysis.  Die
-      BPF-Filter-Syntax (Berkeley Packet Filter) ermöglicht chirurgisch  präzise
-      Packet-Selektion: "host 192.168.1.1 and port 80". Besonders  wertvoll:
-      Memory-effiziente Capture auch bei High-Speed-Interfaces, 
-      ASCII/Hex-Output für Quick-Analysis, Timestamp-Precision für 
-      Timeline-Correlation. Ring-Buffer-Mode rotiert Captures automatisch.  Die
-      libpcap-Basis macht Captures kompatibel zu allen Major-Analysis-Tools. 
-      Remote-Capture via SSH-Tunneling für Network-Forensics. Integration in 
-      Scripts für automatisierte Collection. Besonders geschätzt: die
-      Stabilität  auch bei defekten Frames, minimaler CPU-Overhead, verfügbar
-      auf jedem  Unix-System ohne Installation. Der Syntax mag archaisch wirken,
-      aber die  Effizienz ist unübertroffen. Basis-Skill für jeden
-      Netzwerk-Forensiker  und oft einziges Tool in restricted Environments.
+      Fundamentaler Packet-Sniffer mit BPF-Filter-Syntax für chirurgisch präzise
+      Packet-Selektion seit 1987. Memory-effiziente Capture bei
+      High-Speed-Interfaces, Ring-Buffer-Mode rotiert automatisch. libpcap-Basis
+      macht Captures kompatibel zu allen Analysis-Tools. Verfügbar auf jedem
+      Unix-System.
     skillLevel: intermediate
     url: https://www.tcpdump.org
     icon: 🐙
@@ -4548,21 +4097,11 @@ tools:
   - name: x64dbg
     type: software
     description: >-
-      Der kostenlose Windows-Debugger revolutioniert Malware-Analysis durch 
-      intuitive GUI und Plugin-Ecosystem. Ersetzt den veralteten OllyDbg  mit
-      moderner 64-Bit-Architektur und Active-Development. Besonders  stark:
-      Anti-Anti-Debug-Plugins umgehen Evasion-Techniken,  Script-Engine
-      automatisiert repetitive Tasks, Memory-Map-Viewer  zeigt Process-Layout
-      übersichtlich. Die Plugin-Community entwickelt  ständig neue Extensions:
-      Unpacker für gepackte Malware, Crypto- Finder identifiziert
-      Verschlüsselungsroutinen, API-Logger trackt  System-Calls.
-      Integrated-Disassembler mit Syntax-Highlighting.  Conditional-Breakpoints
-      für Complex-Debugging-Scenarios.  Multi-Threading-Support für moderne
-      Malware. Export-Funktionen  für weitere Analysis. Die portable Version
-      läuft von USB-Stick.  Besonders wertvoll: Zero-Cost für Hobbyisten und
-      kleine Teams  bei Professional-Level-Features. Active-Community in
-      Discord/GitHub  hilft bei Problemen. Der De-facto-Standard für
-      Windows-Malware- Dynamic-Analysis ohne Budget-Constraints.
+      Kostenloser Windows-Debugger mit moderner 64-Bit-Architektur und aktivem
+      Plugin-Ecosystem. Anti-Anti-Debug-Plugins umgehen Evasion-Techniken,
+      Script-Engine automatisiert repetitive Tasks. Memory-Map-Viewer,
+      Conditional-Breakpoints und Multi-Threading-Support für moderne
+      Malware-Dynamic-Analysis.
     skillLevel: advanced
     url: https://x64dbg.com
     icon: 🐛
@@ -4576,56 +4115,52 @@ tools:
     platforms:
       - Windows
     accessType: download
-    license: GPL v3
+    license: GPL v3
     knowledgebase: false
   - name: grep
     type: software
     description: >-
-      Klassisches Unix-Werkzeug zur Zeilenfilterung, entwickelt 1973 von
-      Ken Thompson zur schnellen Suche nach regulären Ausdrücken in Dateien oder
-      Datenströmen. Der Name leitet sich vom ed‑Kommando „g/re/p“ ab und
-      spiegelt die Funktionsweise wider: globaler Ausdruck, Zeile drucken.
-      GNU grep unterstützt drei Regex‑Dialekte (BRE, ERE, PCRE),
-      Farb‑Highlighting und Zero‑Copy‑Block‑Pufferung für Höchstgeschwindigkeit
-      auch in riesigen Logs. Dank seiner Pipe‑Kompatibilität ist grep
-      Grundbaustein unzähliger DFIR‑Einzeiler zum Parsen von Artefakten,
-      Netzwerk‑Flows und Memory‑Dumps.
-    skillLevel: beginner
+      Filtert forensische Logs und Speicher-Dumps durch leistungsstarke reguläre
+      Ausdrücke für präzise Beweissuche. GNU-Implementierung unterstützt drei
+      Regex-Dialekte und Farb-Highlighting für schnelle Mustererkennung.
+      Zero-Copy-Block-Pufferung ermöglicht Höchstgeschwindigkeit auch in
+      riesigen Dateien. Pipe-Kompatibilität macht grep zum Grundbaustein
+      unzähliger DFIR-Einzeiler für Artefakt-Parsing und Netzwerk-Flow-Analyse.
+      Verfügbar auf jeder Unix-Plattform ohne Installation.
     url: https://www.gnu.org/software/grep/
-    icon: 🔍
+    skillLevel: beginner
     domains:
       - incident-response
       - static-investigations
     phases:
       - examination
       - analysis
+    platforms:
+      - Linux
+      - macOS
+      - Windows
     tags:
-      - commandline
+      - cli
       - regular-expressions
       - piping
       - filtering
     related_concepts:
       - Regular Expressions (Regex)
-    platforms:
-      - Linux
-      - macOS
-      - Windows
-    accessType: download
+    icon: 🔍
     license: "GPL\_v3"
-    knowledgebase: false
+    accessType: download
   - name: md5sum / sha256sum
     type: software
     description: >-
-      Klassische Kommandozeilen‑Checksummengeneratoren aus den GNU coreutils,
-      die schnell kryptografische Prüfsummen (MD5 bzw. SHA‑256) berechnen und
-      verifizieren können. Ideal zur Integritätsprüfung von Forensik‑Images,
-      Log‑Archiven und Download‑Artefakten; auch als Stream‑Filter einsetzbar
-      („cat image.dd | sha256sum“). Minimalistischer Funktionsumfang, aber auf
-      nahezu jeder Unix‑ähnlichen Plattform vorinstalliert und damit überall
-      verfügbar.
-    skillLevel: beginner
+      Berechnet kryptografische Prüfsummen für Integritätsprüfung forensischer
+      Images und Artefakte mit minimaler Systembelastung. Verifiziert
+      Chain-of-Custody durch Hash-Vergleiche vor und nach Analysen.
+      Stream-Filter-Fähigkeit ermöglicht simultane Hash-Berechnung während
+      Disk-Imaging. Auf nahezu jeder Unix-Plattform vorinstalliert, daher
+      überall verfügbar für spontane Verifizierungen. Erzeugt standardkonforme
+      Prüfsummen für gerichtliche Verwertbarkeit.
     url: https://www.gnu.org/software/coreutils/
-    icon: 🔢
+    skillLevel: beginner
     domains:
       - incident-response
       - static-investigations
@@ -4633,117 +4168,115 @@ tools:
     phases:
       - examination
       - analysis
-    tags:
-      - commandline
-      - hashing
-      - integrity
-    related_concepts:
-      - Hash Functions & Digital Signatures
     platforms:
       - Linux
       - macOS
       - Windows
-    accessType: Linux (GNU-Utils)
-    license: GPL v3
-    knowledgebase: false
+    tags:
+      - cli
+      - hashing
+      - integrity
+    related_concepts:
+      - Hash Functions & Digital Signatures
+    icon: 🔢
+    license: GPL-3.0
   - name: hashdeep
     type: software
     description: >-
-      Multithread‑fähiges Audit‑Werkzeug, das Dateien rekursiv einliest und
-      gleich mehrere Hash‑Algorithmen (MD5, SHA‑1, SHA‑256, Tiger u.a.) erzeugt.
-      Unterstützt „baseline auditing“ zum automatischen Wieder­erkennen neuer
-      oder veränderter Dateien und kann Hash‑Listen in NIST‑NSRL‑ oder
-      CSV‑Format ausgeben – perfekt für große Datenträger‑Batches.
-    skillLevel: intermediate
+      Erstellt rekursive Hash-Sammlungen für umfangreiche Datenträger-Audits mit
+      mehreren Hash-Algorithmen parallel. Baseline-Auditing erkennt automatisch
+      neue oder veränderte Dateien bei wiederkehrenden Untersuchungen.
+      Multithread-Architektur beschleunigt Hash-Berechnung großer Dateimengen
+      erheblich. Ausgabe in NIST-NSRL oder CSV-Formaten für
+      Datenbank-Integration. Batch-Export ermöglicht automatisierte
+      Deduplizierung und Known-Good-Filterung in Enterprise-Umgebungen.
     url: https://github.com/jessek/hashdeep
-    icon: 🏷️
+    skillLevel: intermediate
     domains:
       - static-investigations
       - cloud-forensics
     phases:
       - examination
       - analysis
-    tags:
-      - hashing
-      - auditing
-      - multihash
-    related_concepts: null
     platforms:
       - Linux
       - macOS
       - Windows
-    accessType: download
+    tags:
+      - hashing
+      - auditing
+      - multihash
+    icon: 🏷️
     license: Public Domain
-    knowledgebase: false
+    accessType: download
   - name: ssdeep
     type: software
     description: >-
-      Tool zur Berechnung „fuzzy hashes“ (Context Triggered Piecewise Hashing,
-      CTPH) für Ähnlichkeits­analysen von Dateien. Kann binäre
-      Malware‑Varianten, Dokumentschablonen oder Logs selbst bei nur teilweisen
-      Überschneidungen matchen. Ausgabe‑Hashes lassen sich in Datenbanken oder
-      YARA‑Regeln integrieren.
-    skillLevel: intermediate
+      Berechnet Fuzzy-Hashes für Ähnlichkeitsanalysen zwischen Malware-Varianten
+      und Dokumentvorlagen ohne exakte Übereinstimmung. Context Triggered
+      Piecewise Hashing erkennt auch teilweise veränderte Dateien zuverlässig.
+      Ausgabe-Hashes integrieren sich in YARA-Regeln und
+      Threat-Intelligence-Datenbanken. Unverzichtbar für
+      Malware-Familie-Zuordnung und Kampagnen-Tracking. Ergänzt traditionelle
+      kryptografische Hashes um Ähnlichkeitsdimension.
     url: https://ssdeep-project.github.io/ssdeep/
-    icon: 🔍
+    skillLevel: intermediate
     domains:
       - malware-analysis
       - incident-response
     phases:
       - analysis
-    tags:
-      - fuzzy-hashing
-      - similarity
-    related_concepts: null
     platforms:
       - Linux
       - macOS
       - Windows
-    accessType: download
+    tags:
+      - fuzzy-hashing
+      - similarity
+    icon: 🔍
     license: "GPL\_v2"
-    knowledgebase: false
+    accessType: download
   - name: hashcat
     type: software
     description: >-
-      Höchstperformanter Passwort‑Recovery‑Accelerator, der CPUs, GPUs, FPGAs
-      und sogar ASICs parallel nutzen kann. Unterstützt über 300 Hash‑Formate
-      (u.a. bcrypt, NTLM, Kerberos, WPA‑PMKID) und besitzt flexible Angriffsmodi
-      (Dictionary, Mask, Hybrid, Rule‑Based). Dank OpenCL‑Backend skaliert
-      hashcat von Laptop‑GPU bis zu verteilten Clustern – unverzichtbar für
-      Credential‑Audits und Incident‑Response.
-    skillLevel: advanced
+      Knackt Passwort-Hashes durch GPU-beschleunigte Brute-Force-Angriffe für
+      Credential-Recovery in Ermittlungen. Unterstützt über 300 Hash-Formate von
+      bcrypt bis Kerberos mit flexiblen Angriffsmodi. OpenCL-Backend nutzt CPUs,
+      GPUs und FPGAs parallel für maximale Performance. Dictionary-, Mask- und
+      Hybrid-Attacken decken verschiedene Passwort-Strategien ab. Unverzichtbar
+      für Credential-Audits und Incident-Response bei Passwort-Diebstahl.
     url: https://hashcat.net/hashcat/
-    icon: ⚡
+    skillLevel: advanced
     domains:
       - incident-response
       - fraud-investigation
     phases:
       - analysis
+    platforms:
+      - Linux
+      - Windows
+      - macOS
     tags:
       - password-recovery
       - gpu-acceleration
       - cracking
       - scenario:credential_theft
-    related_concepts: null
-    platforms:
-      - Linux
-      - Windows
-      - macOS
-    accessType: download
+    icon: ⚡
     license: MIT
-    knowledgebase: false
+    accessType: download
   - name: Linkurious
     type: software
     description: >-
-      Intuitive Graph‑Visualisierungs‑ und Analyseplattform, die
-      Graphdatenbanken wie Neo4j überlagert und Ermittler:innen dabei
-      unterstützt, versteckte Beziehungen in Betrugs‑, AML‑ und
-      Sicherheitsfällen aufzudecken. Leistungsstarke Filter, Geo‑ und
-      Zeitachsenansichten sowie Automatisierungsvorlagen erleichtern das Hunten
-      komplexer Netzwerke und das Erstellen aussagekräftiger Beweisgrafiken.
-    skillLevel: intermediate
+      Visualisiert komplexe Beziehungsnetzwerke in Betrugs- und
+      Geldwäsche-Ermittlungen durch intuitive Graph-Analyse-Oberfläche.
+      Überlagert Neo4j-Datenbanken mit leistungsstarken Filtern und
+      Geo-Ansichten für versteckte Verbindungen. Zeitachsen-Analysen zeigen
+      Entwicklung krimineller Netzwerke über Zeit. Automatisierungsvorlagen
+      beschleunigen wiederkehrende Untersuchungen. Erstellt aussagekräftige
+      Beweisgrafiken für Gerichtspräsentationen. Speziell für Finanzermittlungen
+      und Anti-Geldwäsche-Compliance entwickelt.
     url: https://linkurious.com/
-    icon: 🕸️
+    skillLevel: intermediate
     domains:
       - fraud-investigation
       - network-forensics
@@ -4751,84 +4284,98 @@ tools:
     phases:
       - analysis
       - reporting
+    platforms:
+      - Web
+      - Linux
     tags:
       - graph-visualisierung
       - link-analysis
       - aml
       - fraud
-    related_concepts: null
-    platforms:
-      - Web
-      - Linux
-    accessType: commercial
+    icon: 🕸️
     license: Proprietary
-    knowledgebase: false
+    accessType: commercial
   - name: Android Studio
     type: software
-    description: "Offizielle IDE von Google für die Android‑Entwicklung, basierend auf JetBrains\_IntelliJ\_IDEA. Integriert ein flexibles Gradle‑Buildsystem, Jetpack‑Compose‑Designer, Geräte‑Emulator, Profiler und Debugging‑Tools, um mobile Apps effizient zu erstellen, zu testen und zu analysieren – auch für forensische Re‑Packaging‑ oder Malware‑Analysen von APKs."
-    skillLevel: intermediate
+    description: >-
+      Analysiert Android-APK-Dateien und erstellt forensische
+      Re-Packaging-Umgebungen für Mobile-Malware-Untersuchungen. Integrierter
+      Geräte-Emulator ermöglicht sichere Malware-Detonation ohne
+      Hardware-Gefährdung. Gradle-Build-System rekompiliert modifizierte Apps
+      für Behavioral-Analysis. Profiler und Debugging-Tools tracken verdächtige
+      Aktivitäten in Echtzeit. Layout-Inspector analysiert
+      UI-Manipulation-Techniken. ADB-Integration für direkte Geräte-Forensik und
+      App-Extraktion.
     url: https://developer.android.com/studio
-    icon: 📱
+    skillLevel: intermediate
     domains:
       - mobile-forensics
       - malware-analysis
     phases:
       - examination
       - analysis
+    platforms:
+      - Windows
+      - macOS
+      - Linux
     tags:
       - ide
       - android
       - emulator
       - debugging
-    related_concepts: null
-    platforms:
-      - Windows
-      - macOS
-      - Linux
-    accessType: download
+    icon: 📱
     license: Freeware
-    knowledgebase: false
+    accessType: download
   - name: ADB
     type: software
     description: >-
-      Vielseitiges Client‑Server‑Kommandozeilenwerkzeug, mit dem Fachleute über
-      USB oder Netzwerk mit Android‑Geräten und Emulatoren kommunizieren: Pakete
-      installieren, Dateien extrahieren, Logcats erfassen, Ports weiterleiten
-      und logische wie physische Datensicherungen erstellen. Unverzichtbar für
-      mobile Forensik, Incident‑Response und App‑Entwicklung.
-    skillLevel: intermediate
+      Kommuniziert mit Android-Geräten für forensische Datenextraktion über USB
+      oder Netzwerk ohne Root-Zugriff. Erstellt logische Backups von App-Daten,
+      installiert forensische Analysewerkzeuge, erfasst Live-Logcats für
+      Incident-Response. Port-Weiterleitung ermöglicht sichere Remote-Analyse.
+      File-Transfer-Funktionen extrahieren Beweise direkt vom Gerät.
+      Shell-Access für erweiterte Forensik-Kommandos. Unverzichtbar für
+      Mobile-Incident-Response und App-Entwicklungs-Forensik.
     url: https://developer.android.com/tools/adb
-    icon: 🔌
+    skillLevel: intermediate
     domains:
       - mobile-forensics
       - incident-response
     phases:
       - data-collection
       - examination
+    platforms:
+      - Windows
+      - macOS
+      - Linux
     tags:
       - cli
       - mobile
       - device-management
       - extraction
-    related_concepts: null
-    platforms:
-      - Windows
-      - macOS
-      - Linux
-    accessType: download
+    icon: 🔌
     license: Freeware
-    knowledgebase: false
+    accessType: download
   - name: dc3dd
     type: software
-    description: "dc3dd ist eine forensisch erweiterte Variante des klassischen Unix‑Befehls dd. Sie unterstützt das gleichzeitige Berechnen mehrerer Hash‑Werte (MD5, SHA‑1, SHA‑256, SHA‑512) während der Image‑Erstellung, führt ein detailliertes Log mit Prüfsummen und Fehlerblöcken und zeigt einen Fortschrittsbalken an. Weitere Funktionen sind das Splitten großer Abbilder, das gezielte Überschreiben von Mustern zum sicheren Löschen\_und die Möglichkeit, Fehlersektoren separat auszugeben, was besonders bei defekten Medien hilfreich ist."
-    skillLevel: intermediate
+    description: >-
+      Erstellt militärstandard-konforme Festplatten-Images mit detailliertem
+      forensischen Logging für Strafverfolgung. Simultane Multi-Hash-Berechnung
+      und segmentweise Verifizierung gewährleisten Beweisintegrität. Umfassendes
+      Error-Logging dokumentiert jeden Sektor-Lesefehler für
+      Chain-of-Custody-Compliance. Pattern-Fill-Funktionen für DoD-konforme
+      sichere Löschung. Split-Archive-Unterstützung mit Resume-Fähigkeit für
+      unterbrochene Long-Running-Images.
     url: https://sourceforge.net/projects/dc3dd/
-    icon: 💾
+    skillLevel: intermediate
     domains:
       - incident-response
       - static-investigations
     phases:
       - data-collection
+    platforms:
+      - Linux
+      - Windows
     tags:
       - disk-imaging
       - hashing
@@ -4837,52 +4384,52 @@ tools:
       - cli
     related_concepts:
       - Hash Functions & Digital Signatures
-    platforms:
-      - Linux
-      - Windows
-    accessType: download
+    icon: 💾
     license: "GPL\_v2"
-    knowledgebase: false
+    accessType: download
   - name: ddrescue
     type: software
     description: >-
-      GNU ddrescue ist ein spezialisiertes Datenrettungs‑Tool, das beschädigte
-      oder fehlerhafte Blockgeräte sektorweise ausliest und zunächst alle gut
-      lesbaren Bereiche sichert, bevor es sich mehrfach an problematischen
-      Sektoren versucht. Sein Map‑File protokolliert jeden Leseversuch, sodass
-      abgebrochene Wiederherstellungen jederzeit fortgesetzt oder optimiert
-      werden können, ohne bereits gerettete Sektoren erneut zu belasten. Zudem
-      bietet ddrescue einen Fill‑Modus, um schwierige Bereiche gezielt mit
-      Platzhaltern zu überschreiben – nützlich für die Vorbereitung
-      nachträglicher Dateisystem‑Reparaturen.
-    skillLevel: intermediate
+      Rettet Daten von beschädigten Datenträgern durch intelligente
+      Multi-Pass-Strategie für schwierige forensische Akquisitionen. Sichert
+      zunächst alle lesbaren Bereiche, bevor problematische Sektoren mehrfach
+      versucht werden. Map-File protokolliert jeden Leseversuch für
+      Resume-Fähigkeit ohne erneute Medien-Belastung. Fill-Modus bereitet
+      schwierige Bereiche für nachträgliche Dateisystem-Reparaturen vor.
+      Unverzichtbar für defekte Beweismittel-Datenträger.
     url: https://www.gnu.org/software/ddrescue/
-    icon: 🛟
+    skillLevel: intermediate
     domains:
       - static-investigations
       - fraud-investigation
     phases:
       - data-collection
+    platforms:
+      - Linux
+      - macOS
+      - Windows
     tags:
       - data-recovery
       - disk-imaging
       - map-file
       - cli
       - scenario:disk_imaging
-    related_concepts: null
-    platforms:
-      - Linux
-      - macOS
-      - Windows
-    accessType: download
+    icon: 🛟
     license: "GPL\_v2+"
-    knowledgebase: false
+    accessType: download
   - name: REMnux
     type: software
-    description: "REMnux ist eine auf Ubuntu basierende Linux‑Distribution, die eine kuratierte Sammlung frei verfügbarer Tools für Malware‑Analyse, Reverse\_Engineering und Speicherforensik in einer sofort einsatzbereiten VM vereint. Sie erspart Analyst:innen das mühsame Zusammenstellen einzelner Werkzeuge, da Paketverwaltung, Desktop‑Integration und Lab‑Skripte bereits vorkonfiguriert sind. Zu den integrierten Highlights zählen unter anderem Ghidra, Radare2, Volatility, stegdetect sowie diverse Netzwerk‑ und Sandboxing‑Utilities, die regelmäßig in koordinierten Releases aktualisiert werden."
-    skillLevel: intermediate
+    description: >-
+      Ubuntu-basierte Malware-Analyse-Distribution vereint kuratierte
+      Reverse-Engineering-Tools in sofort einsatzbereiter VM-Umgebung.
+      Vorkonfigurierte Integration von Ghidra, Radare2, Volatility und
+      Netzwerk-Analyse-Tools spart Stunden manueller Installation.
+      Spezialisierte Malware-Detonation-Umgebung mit Netzwerk-Simulation und
+      Traffic-Analyse. Regelmäßige Updates halten Tool-Sammlung aktuell.
+      Docker-Integration für sichere Malware-Sandboxing. Paketverwaltung
+      vereinfacht Tool-Erweiterung.
     url: https://remnux.org/
-    icon: 🐧
+    skillLevel: intermediate
     domains:
       - malware-analysis
       - incident-response
@@ -4890,50 +4437,60 @@ tools:
     phases:
       - examination
       - analysis
+    platforms:
+      - Linux
     tags:
       - linux-distro
       - toolkit
       - reverse-engineering
       - virtualization
-    related_concepts: null
-    platforms:
-      - Linux
-    accessType: download
+    icon: 🐧
     license: mixed-oss
-    knowledgebase: false
+    accessType: download
   - name: Android Backup Extractor
     type: software
-    description: "Das bewährte Kommandozeilen‑Werkzeug zum Entpacken und Packen von Android‑Backups (.ab) aus »adb backup«\_– inklusive Entschlüsselung passwort­geschützter Archive dank integrierter AES‑Routinen. Ideal für die Extraktion forensisch relevanter App‑Daten ohne Root‑Zugriff und damit ein Must‑have für Mobile‑Analysen. :contentReference[oaicite:0]{index=0}"
-    skillLevel: intermediate
+    description: >-
+      Entpackt und entschlüsselt Android-Backup-Archive aus ADB-Kommandos für
+      forensische App-Daten-Analyse ohne Root-Zugriff. Integrierte AES-Routinen
+      dekryptieren passwortgeschützte Backups automatisch. Extrahiert versteckte
+      App-Sandboxes und Systemdaten aus .ab-Dateien. Java-basierte Architektur
+      funktioniert plattformübergreifend. Batch-Processing für
+      Multiple-Device-Analysen. Unverzichtbares Werkzeug für Mobile-Forensik bei
+      eingeschränkten Extraktionsmöglichkeiten.
     url: https://github.com/nelenkov/android-backup-extractor
-    icon: 📦
+    skillLevel: intermediate
     domains:
       - mobile-forensics
       - static-investigations
     phases:
       - examination
       - analysis
+    platforms:
+      - Windows
+      - Linux
+      - macOS
     tags:
-      - commandline
+      - cli
       - backup
       - encryption
       - artifact-extraction
       - android
       - scenario:file_recovery
-    related_concepts: null
-    platforms:
-      - Windows
-      - Linux
-      - macOS
+    icon: 📦
+    license: Apache-2.0
     accessType: download
-    license: "Apache\_2.0"
-    knowledgebase: false
   - name: CAINE
     type: software
-    description: "CAINE (Computer Aided INvestigative Environment) ist eine Ubuntu‑basierte Live‑Linux‑Distribution, die mehr als 150 Forensik‑Werkzeuge in einer schreibgeschützten Umgebung bündelt. Version 14.0 »Lightstream« (März\_2025) bringt Kernel\_6.8, UEFI‑Support und das UnBlock‑GUI zum gezielten Aufheben der Write‑Block‑Funktion für einzelne Devices. :contentReference[oaicite:2]{index=2}"
-    skillLevel: beginner
+    description: >-
+      Ubuntu-basierte Live-Forensik-Distribution bündelt über 150 Werkzeuge in
+      schreibgeschützter Umgebung für kontaminationsfreie Ermittlungen.
+      UnBlock-GUI ermöglicht gezieltes Aufheben der Write-Block-Funktion für
+      einzelne Geräte. UEFI-Support und Kernel 6.8 gewährleisten moderne
+      Hardware-Kompatibilität. Forensics-Menü organisiert Tools nach
+      Untersuchungsphasen. Live-Boot ohne Installation schützt Host-System vor
+      Kontamination.
     url: https://www.caine-live.net/
-    icon: 💿
+    skillLevel: beginner
     domains:
       - incident-response
       - static-investigations
@@ -4943,73 +4500,75 @@ tools:
       - data-collection
       - examination
       - analysis
+    platforms:
+      - Linux
+    domain-agnostic-software:
+      - specific-os
     tags:
       - live-distro
       - imaging
       - write-blocking
       - gui
-      - commandline
+      - cli
       - scenario:disk_imaging
-    related_concepts: null
-    platforms:
-      - Linux
-    accessType: download
+    icon: 💿
     license: "LGPL\_2.1+"
-    knowledgebase: false
+    accessType: download
   - name: Aircrack-ng
     type: software
     description: >-
-      Die legendäre WLAN-Security-Suite vereint seit 2006 alle Tools für 
-      802.11-Penetration-Testing und Forensic-WLAN-Analysis. Airodump-ng 
-      sammelt Packets und zeigt Hidden-Networks, Aireplay-ng injiziert 
-      Deauth-Frames für Handshake-Capture, Aircrack-ng selbst bricht  WEP-Keys
-      in Minuten und WPA2-PSKs mit Dictionary-Attacks. Besonders  wertvoll für
-      Forensik: Packet-Capture-Analysis identifiziert  Rogue-APs,
-      Client-Probing-Behavior enthüllt Bewegungsprofile,  WPS-PIN-Recovery bei
-      Legacy-Routern. Die GPU-Acceleration  (via pyrit/hashcat) beschleunigt
-      PSK-Cracking dramatisch.  Support für moderne Standards:
-      WPA3-Transition-Mode, 802.11ac/ax.  Scripting-Interface automatisiert
-      Mass-Assessments.  Cross-Platform-Verfügbarkeit mit
-      Hardware-Treiber-Support.  Forensic-Mode bewahrt Evidence-Integrity.
-      Integration in  Penetration-Testing-Frameworks. Der Klassiker für
-      WLAN-Forensik  wenn moderne Enterprise-WLANs kompromittiert wurden.
-    skillLevel: advanced
+      Umfassende WLAN-Forensik-Suite analysiert 802.11-Verkehr und identifiziert
+      Sicherheitsverletzungen in drahtlosen Netzwerken. Airodump-ng sammelt
+      Pakete und deckt versteckte Netzwerke auf, Aireplay-ng injiziert
+      Deauth-Frames für Handshake-Erfassung. WEP-Schlüssel-Rekonstruktion in
+      Minuten, WPA2-PSK-Recovery mit Dictionary-Angriffen.
+      Rogue-Access-Point-Erkennung und Client-Probing-Analyse für
+      Bewegungsprofile. GPU-Beschleunigung via hashcat für moderne
+      Verschlüsselungsstandards.
     url: https://www.aircrack-ng.org/
-    icon: 📦
+    skillLevel: advanced
     domains:
       - network-forensics
       - incident-response
     phases:
       - data-collection
       - analysis
-    tags:
-      - commandline
-      - wireless
-      - packet-capture
-      - injection
-      - cracking
-      - scenario:credential_theft
-    related_concepts: null
     platforms:
       - Windows
       - Linux
       - macOS
       - BSD
-    accessType: download
+    tags:
+      - cli
+      - wireless
+      - packet-capture
+      - injection
+      - cracking
+      - scenario:credential_theft
+    icon: 📦
     license: "GPL\_v2\_(+\_BSD/OpenSSL\_components)"
-    knowledgebase: false
+    accessType: download
   - name: WiFi Pineapple
     type: software
-    description: "Die Hak5‑Hardware liefert mit der patentierten PineAP‑Suite einen vollwertigen Rogue‑Access‑Point: automatisches Recon, Handshake‑ und Enterprise‑Credential‑Capture, gezielte Client‑Filterung sowie Cloud\_C²‑Fernsteuerung\_– alles per browser­basierter GUI auf dem Mark\_VII oder Enterprise‑Modell. :contentReference[oaicite:6]{index=6}"
-    skillLevel: intermediate
+    description: >-
+      Spezialisierte Rogue-Access-Point-Hardware für WLAN-Penetrationstests und
+      forensische Netzwerk-Analyse. PineAP-Suite führt automatische
+      Reconnaissance durch, erfasst Handshakes und Enterprise-Credentials.
+      Gezielte Client-Filterung und Cloud-C2-Fernsteuerung für diskrete
+      Operationen. Browser-basierte GUI vereinfacht komplexe WLAN-Angriffe. Mark
+      VII und Enterprise-Modelle für verschiedene Einsatzszenarien.
+      Unverzichtbar für WLAN-Sicherheitsbewertungen und Incident-Response.
     url: https://shop.hak5.org/products/wifi-pineapple
-    icon: 📡
+    skillLevel: intermediate
     domains:
       - network-forensics
       - incident-response
     phases:
       - data-collection
       - analysis
+    platforms:
+      - Web
+      - Hardware
     tags:
       - rogue-ap
       - wireless
@@ -5017,13 +4576,2590 @@ tools:
       - gui
       - web-interface
       - scenario:remote_access
-    related_concepts: null
-    platforms:
-      - Web
-      - Hardware
-    accessType: purchase
+    icon: 📡
     license: Proprietär
+  - name: Network Protocols & Packet Analysis
+    type: concept
+    description: >-
+      Fundamentale Kenntnisse der Netzwerkprotokolle von Layer 2-7 für
+      Traffic-Forensik und Incident-Response. TCP/IP-Grundlagen,
+      HTTP/HTTPS-Header-Analyse, DNS-Tunneling-Erkennung, und
+      Protokoll-Anomalie-Detection. Session-Rekonstruktion aus PCAP-Dateien,
+      Payload-Extraktion und C2-Kommunikations-Patterns. Verschlüsselte
+      Protokolle wie TLS, SSH, VPN-Traffic-Charakteristika.
+      OSI-Model-Verständnis essentiell für Netzwerk-Forensik und APT-Hunting.
+    skillLevel: intermediate
+    url: https://www.iana.org/protocols
+    icon: 🌐
+    domains:
+      - incident-response
+      - network-forensics
+      - malware-analysis
+      - cloud-forensics
+    phases:
+      - examination
+      - analysis
+    tags:
+      - protocol-analysis
+      - packet-inspection
+      - session-reconstruction
+      - c2-analysis
+      - traffic-patterns
+      - network-baseline
+      - payload-extraction
+      - anomaly-detection
+    knowledgebase: true
+  - name: File Systems & Storage Forensics
+    type: concept
+    description: >-
+      Tiefgreifendes Verständnis von Dateisystem-Strukturen für forensische
+      Datenrekonstruktion. NTFS-Metadaten ($MFT, $LogFile, $UsnJrnl),
+      ext4-Journaling, APFS-Snapshots und HFS+-Forensik. Slack-Space-Analyse,
+      Bad-Cluster-Hiding, Alternate-Data-Streams und versteckte Partitionen.
+      Deleted-File-Recovery durch Inode-Strukturen, File-Carving-Prinzipien und
+      Timestamp-Manipulation-Detection. Cloud-Storage-Forensik für OneDrive,
+      Google Drive, Dropbox-Artefakte. Volume-Shadow-Copy-Analyse für
+      Windows-Timeline-Rekonstruktion.
+    skillLevel: intermediate
+    url: https://forensicswiki.xyz/wiki/index.php?title=File_Systems
+    icon: 💾
+    domains:
+      - static-investigations
+      - incident-response
+      - mobile-forensics
+      - cloud-forensics
+    phases:
+      - examination
+      - analysis
+    tags:
+      - filesystem-analysis
+      - metadata-extraction
+      - deleted-data-recovery
+      - slack-space
+      - journaling-analysis
+      - timestamp-forensics
+      - partition-analysis
+      - cloud-storage
+    knowledgebase: true
+  - name: Timeline Analysis & Event Correlation
+    type: concept
+    description: >-
+      Methodische Korrelation zeitbasierter Artefakte für
+      Incident-Rekonstruktion und Beweisführung. Super-Timeline-Erstellung aus
+      heterogenen Quellen, Zeitstempel-Normalisierung und Timezone-Handling.
+      Pivot-Point-Identifikation, Activity-Gap-Analyse und
+      Behavioral-Pattern-Erkennung. Correlation-Techniken zwischen
+      System-Events, User-Activity und Network-Connections.
+      Anti-Forensik-Detection durch Timeline-Inconsistencies.
+      Automated-Timeline- Processing und Machine-Learning-basierte
+      Anomalie-Erkennung für Enterprise-Scale-Investigations.
+    skillLevel: advanced
+    url: https://www.sans.org/white-papers/digital-forensics-timeline-analysis/
+    icon: ⏰
+    domains:
+      - incident-response
+      - static-investigations
+      - network-forensics
+      - cloud-forensics
+    phases:
+      - analysis
+      - reporting
+    tags:
+      - timeline-correlation
+      - event-sequencing
+      - temporal-analysis
+      - super-timeline
+      - pivot-points
+      - behavioral-patterns
+      - anomaly-detection
+      - anti-forensics-detection
+    knowledgebase: true
+  - name: Memory Forensics & Process Analysis
+    type: concept
+    description: >-
+      Volatile-Memory-Strukturen und Process-Internals für
+      Advanced-Malware-Detection. Virtual-Memory-Layout,
+      Process-Injection-Techniken, DLL-Hollowing und Process-Ghosting-Erkennung.
+      Kernel-Structures-Analysis, System-Call-Hooking und
+      Rootkit-Detection-Methoden. Memory-Dump-Acquisition-Strategien,
+      Address-Space-Reconstruction und Encrypted-Memory-Handling. 
+      Cross-Platform-Memory-Forensik für Windows, Linux, macOS-Systeme.
+      Live-Memory-Analysis vs. Dead-Memory-Investigation-Tradeoffs.
+    skillLevel: advanced
+    url: >-
+      https://volatility-labs.blogspot.com/2012/10/movp-31-memory-forensics-taxonomy.html
+    icon: 🧠
+    domains:
+      - incident-response
+      - malware-analysis
+      - static-investigations
+    phases:
+      - examination
+      - analysis
+    tags:
+      - memory-structures
+      - process-injection
+      - rootkit-detection
+      - kernel-analysis
+      - address-space
+      - live-analysis
+      - malware-hiding
+      - system-internals
+    knowledgebase: true
+  - name: hdiutil
+    type: software
+    description: >-
+      macOS-nativer Disk-Image-Manager erstellt und mountet DMG, ISO,
+      IMG-Dateien für forensische Analyse. Besonders wertvoll:
+      Sparse-Image-Creation für Live-Collection, Checksum-Verifizierung mit
+      CRC32/MD5, Read-Only-Mounting verhindert Beweis-Kontamination.
+      Komprimierung und Verschlüsselung für sichere Evidence-Storage.
+      Integration in Automator-Workflows für Batch-Processing. Der Standard für
+      macOS-Imaging ohne externe Tools.
+    skillLevel: intermediate
+    url: https://ss64.com/osx/hdiutil.html
+    icon: 💿
+    domains:
+      - incident-response
+      - static-investigations
+    phases:
+      - data-collection
+      - examination
+    platforms:
+      - macOS
+    accessType: built-in
+    license: Proprietary
     knowledgebase: false
+    tags:
+      - cli
+      - disk-imaging
+      - mounting
+      - apfs
+      - compression
+      - encryption
+      - checksum
+      - sparse-images
+    related_concepts:
+      - Hash Functions & Digital Signatures
+      - Digital Evidence Chain of Custody
+    related_software:
+      - dd
+      - Fuji
+  - name: asr
+    type: software
+    description: >-
+      Apple Software Restore führt Block-Level-Restores von macOS-Images durch
+      und ermöglicht forensisches Cloning ganzer Volumes. Besonders bei
+      APFS-Container-Forensik wertvoll: Bit-genaue Duplikation inklusive
+      Metadaten, Snapshot-Preservation und FileVault-Verschlüsselung.
+      Network-Restore-Fähigkeiten für Remote-Imaging. Der Low-Level-Zugriff
+      umgeht Dateisystem-Beschränkungen für saubere Akquisition.
+    skillLevel: advanced
+    url: https://ss64.com/osx/asr.html
+    icon: 🔄
+    domains:
+      - incident-response
+      - static-investigations
+    phases:
+      - data-collection
+    platforms:
+      - macOS
+    accessType: built-in
+    license: Proprietary
+    knowledgebase: false
+    tags:
+      - cli
+      - disk-imaging
+      - cloning
+      - apfs
+      - filevault
+      - network-restore
+      - block-level
+    related_concepts:
+      - Digital Evidence Chain of Custody
+    related_software:
+      - hdiutil
+      - dd
+  - name: plutil
+    type: software
+    description: >-
+      Property-List-Parser konvertiert zwischen XML, Binary und JSON-Formaten
+      für macOS-Artefakt-Analyse. Extrahiert Konfigurationsdaten aus Apps,
+      System-Preferences und versteckten Plist-Files. Besonders wertvoll:
+      Binary-Plist-Dekodierung ohne Xcode, Syntax-Validation für korrupte Files,
+      Batch-Conversion für Massen-Analyse. Command-Line-Integration in
+      Forensik-Scripts. Unverzichtbar für macOS-Application-Forensik.
+    skillLevel: beginner
+    url: https://ss64.com/osx/plutil.html
+    icon: 📋
+    domains:
+      - static-investigations
+      - incident-response
+    phases:
+      - examination
+      - analysis
+    platforms:
+      - macOS
+    accessType: built-in
+    license: Proprietary
+    knowledgebase: false
+    tags:
+      - cli
+      - plist-parser
+      - xml-conversion
+      - json-export
+      - artifact-extraction
+      - batch-processing
+      - macos-artifacts
+    related_concepts:
+      - File Systems & Storage Forensics
+    related_software:
+      - ExifTool
+  - name: spotlight_parser
+    type: software
+    description: >-
+      Python-Framework analysiert macOS-Spotlight-Index für versteckte
+      Dateimetadaten und Suchhistorie. Extrahiert gelöschte Referenzen,
+      Volltext-Indizes und User-Search-Patterns aus .store-Datenbanken.
+      Rekonstruiert File-Timeline auch nach Löschung, identifiziert externe
+      Volume-Verbindungen. Besonders wertvoll für User-Activity-Tracking und
+      Data-Exfiltration-Nachweis auf macOS-Systemen.
+    skillLevel: intermediate
+    url: https://github.com/ydkhatri/spotlight_parser
+    icon: 🔍
+    domains:
+      - static-investigations
+      - incident-response
+    phases:
+      - examination
+      - analysis
+    platforms:
+      - macOS
+      - Linux
+      - Windows
+    accessType: download
+    license: MIT
+    knowledgebase: false
+    tags:
+      - cli
+      - spotlight-analysis
+      - metadata-parser
+      - deleted-file-recovery
+      - search-history
+      - python-tool
+      - macos-artifacts
+      - user-activity
+    related_concepts:
+      - File Systems & Storage Forensics
+    related_software:
+      - ALEAPP
+      - iLEAPP
+  - name: FSEventsParser
+    type: software
+    description: >-
+      Dekodiert macOS-FSEvents-Logs für vollständige
+      Dateisystem-Aktivitäts-Timeline ohne Lücken. Trackt Datei-Creation,
+      Modification, Deletion und Movement auch bei gelöschten Files. Besonders
+      wertvoll: Millisecond-Precision-Timestamps, Volume-übergreifende Tracking,
+      Correlation mit anderen Artefakten. Python-basierte Parsing-Engine für
+      Command-Line-Integration. Standard-Tool für macOS-Timeline-Forensik und
+      Incident-Reconstruction.
+    skillLevel: intermediate
+    url: https://github.com/dlcowen/FSEventsParser
+    icon: 📊
+    domains:
+      - static-investigations
+      - incident-response
+    phases:
+      - examination
+      - analysis
+    platforms:
+      - macOS
+      - Linux
+      - Windows
+    accessType: download
+    license: GPL-3.0
+    knowledgebase: false
+    tags:
+      - cli
+      - timeline-analysis
+      - filesystem-monitoring
+      - python-tool
+      - macos-artifacts
+      - deleted-file-recovery
+      - high-precision
+      - correlation-engine
+    related_concepts:
+      - Timeline Analysis & Event Correlation
+      - File Systems & Storage Forensics
+    related_software:
+      - Plaso (log2timeline)
+      - Timesketch
+  - name: chainbreaker
+    type: software
+    description: >-
+      Python-Tool extrahiert Passwörter, Zertifikate und Schlüssel aus
+      macOS-Keychain-Dateien für Credential-Recovery. Dekryptiert sowohl Login-
+      als auch System-Keychains mit User-Passwort oder Master-Key. Besonders
+      wertvoll: WiFi-Passwort-Extraktion, Certificate-Chain-Analysis,
+      Secure-Notes-Dekodierung. Batch-Processing für Enterprise-Deployments.
+      Export in strukturierte Formate für weitere Analyse. Standard für
+      macOS-Credential-Forensik.
+    skillLevel: advanced
+    url: https://github.com/n0fate/chainbreaker
+    icon: ⛓️
+    domains:
+      - static-investigations
+      - incident-response
+    phases:
+      - examination
+      - analysis
+    platforms:
+      - macOS
+      - Linux
+      - Windows
+    accessType: download
+    license: GPL-3.0
+    knowledgebase: false
+    tags:
+      - cli
+      - credential-extraction
+      - keychain-analysis
+      - password-recovery
+      - certificate-analysis
+      - python-tool
+      - macos-artifacts
+      - wifi-passwords
+    related_concepts:
+      - Hash Functions & Digital Signatures
+    related_software:
+      - hashcat
+  - name: UnifiedLogReader
+    type: software
+    description: >-
+      Alpha-Stadium-Parser für macOS-Unified-Logging-System analysiert
+      strukturierte Logd-Datenbanken für System-Event-Reconstruction. Extrahiert
+      versteckte Debug-Messages, Kernel-Events und App-Crashes aus
+      tracev3-Files. Besonders wertvoll bei Malware-Persistenz-Analysis und
+      System-Tampering-Detection. Korreliert Events über Process-Boundaries
+      hinweg. Experimentelle Software mit begrenzter Stability aber einzigartige
+      Capabilities.
+    skillLevel: expert
+    url: https://github.com/ydkhatri/UnifiedLogReader
+    icon: 📜
+    domains:
+      - incident-response
+      - malware-analysis
+    phases:
+      - examination
+      - analysis
+    platforms:
+      - macOS
+      - Linux
+      - Windows
+    accessType: download
+    license: MIT
+    knowledgebase: false
+    tags:
+      - cli
+      - log-parser
+      - unified-logs
+      - python-tool
+      - macos-artifacts
+      - system-events
+      - kernel-analysis
+      - experimental
+    related_concepts:
+      - Timeline Analysis & Event Correlation
+    related_software:
+      - Plaso (log2timeline)
+      - Aftermath
+  - name: xmount
+    type: software
+    description: >-
+      Virtuelles Dateisystem mountet forensische Images als Block-Devices für
+      Live-Analyse ohne Extraktion. Unterstützt EWF, AFF, RAW und DMG-Formate
+      mit gleichzeitiger Konvertierung zwischen Formaten. Besonders wertvoll:
+      Cache-System beschleunigt wiederholte Zugriffe, Write-Cache simuliert
+      beschreibbare Images für Tests. FUSE-basierte Architektur für
+      Linux/macOS-Integration. Perfekt für Timeline-Tools die direkten
+      Disk-Zugriff benötigen.
+    skillLevel: intermediate
+    url: https://www.pinguin.lu/xmount
+    icon: 🗄️
+    domains:
+      - static-investigations
+      - incident-response
+    phases:
+      - examination
+      - analysis
+    platforms:
+      - Linux
+      - macOS
+    accessType: download
+    license: GPL-3.0
+    knowledgebase: false
+    tags:
+      - cli
+      - virtual-filesystem
+      - image-mounting
+      - format-conversion
+      - fuse-based
+      - cache-system
+      - write-simulation
+      - cross-format
+    related_concepts:
+      - File Systems & Storage Forensics
+    related_software:
+      - OSFMount
+      - ewfmount
+  - name: ewfmount
+    type: software
+    description: >-
+      FUSE-Wrapper mountet Expert-Witness-Format-Images als reguläre Files für
+      Standard-Tool-Zugriff. Teil der libewf-Suite ermöglicht Read-Only-Access
+      auf E01/Ex01-Segmente ohne Vollextraktion. Besonders praktisch:
+      Integration in Standard-Workflows, Performance-Optimierung für große
+      Archives, Metadata-Preservation. Cross-Platform-Verfügbarkeit für
+      heterogene Lab-Umgebungen. Alternative zu proprietären Mounting-Tools.
+    skillLevel: beginner
+    url: https://github.com/libyal/libewf/wiki/Mounting
+    icon: 📁
+    domains:
+      - static-investigations
+      - incident-response
+    phases:
+      - examination
+    platforms:
+      - Linux
+      - macOS
+    accessType: download
+    license: LGPL-3.0
+    knowledgebase: false
+    tags:
+      - cli
+      - ewf-mounting
+      - fuse-filesystem
+      - read-only
+      - segment-handling
+      - cross-platform
+      - libewf-suite
+      - performance-optimized
+    related_concepts:
+      - Digital Evidence Chain of Custody
+    related_software:
+      - xmount
+      - ewfacquire
+  - name: iPhone Backup Extractor
+    type: software
+    description: >-
+      Kommerzielle iOS-Backup-Analysis-Suite extrahiert Daten aus
+      iTunes/Finder-Backups inklusive verschlüsselter Archives. Dekodiert
+      App-Sandboxes, Keychain-Items und gelöschte SQLite-Records für umfassende
+      iOS-Forensik. Besonders wertvoll: GUI für nicht-technische Ermittler,
+      automatische App-Erkennung, Timeline-Export. Unterstützt iOS 3-17 mit
+      regelmäßigen Updates. Premium-Features für Location-Data und
+      Advanced-Recovery.
+    skillLevel: beginner
+    url: https://www.3utools.com/iphone-backup-extractor/
+    icon: 📱
+    domains:
+      - mobile-forensics
+      - static-investigations
+    phases:
+      - examination
+      - analysis
+    platforms:
+      - Windows
+      - macOS
+    accessType: commercial
+    license: Proprietary
+    knowledgebase: false
+    tags:
+      - gui
+      - ios-backup
+      - commercial
+      - keychain-extraction
+      - app-analysis
+      - timeline-export
+      - encrypted-backup
+      - sqlite-recovery
+    related_concepts:
+      - SQL
+      - Hash Functions & Digital Signatures
+    related_software:
+      - iLEAPP
+      - Cellebrite UFED
+  - name: tmutil
+    type: software
+    description: >-
+      Time-Machine-Utility steuert macOS-Backup-System und analysiert
+      Snapshot-Strukturen für forensische Timeline-Reconstruction. Listet
+      verfügbare Backups, vergleicht Versionen und extrahiert historische
+      File-States. Besonders wertvoll: Point-in-Time-Recovery für Evidence,
+      Metadata-Analyse von Backup-Changes, APFS-Snapshot-Integration.
+      Thin-Backup-Analysis reduziert Storage-Overhead. Command-Line-Access für
+      Scripting-Integration.
+    skillLevel: intermediate
+    url: https://ss64.com/osx/tmutil.html
+    icon: ⏰
+    domains:
+      - static-investigations
+      - incident-response
+    phases:
+      - examination
+      - analysis
+    platforms:
+      - macOS
+    accessType: built-in
+    license: Proprietary
+    knowledgebase: false
+    tags:
+      - cli
+      - backup-analysis
+      - snapshot-examination
+      - apfs-snapshots
+      - version-comparison
+      - timeline-reconstruction
+      - point-in-time-recovery
+      - metadata-analysis
+    related_concepts:
+      - Timeline Analysis & Event Correlation
+      - File Systems & Storage Forensics
+    related_software:
+      - ShadowExplorer
+      - hdiutil
+  - name: macOS Target Disk Mode Acquisition
+    type: method
+    description: >-
+      Forensische Datensammlung über Target Disk Mode (TDM) durch Drücken der
+      T-Taste beim Boot-Vorgang. Das Asservat verhält sich wie eine externe
+      Festplatte über Firewire/Thunderbolt-Verbindung. Vollständiges Verfahren:
+      1) Hardware-Schreibblocker anschließen, 2) T-Taste beim Boot drücken, 3)
+      Ziel-Mac per Firewire/Thunderbolt verbinden, 4) Imaging mit dd/hdiutil
+      durchführen. Limitierungen: nur erste Festplatte verfügbar,
+      Firmware-Passwort blockiert TDM, Benutzerpasswort für
+      FileVault-Entschlüsselung erforderlich.
+    url: https://cloud.cc24.dev/f/32324
+    skillLevel: intermediate
+    domains:
+      - incident-response
+      - static-investigations
+    phases:
+      - data-collection
+    platforms:
+      - macOS
+    tags:
+      - live-acquisition
+      - target-disk-mode
+      - hardware-connection
+      - write-blocker-required
+      - firmware-limitation
+      - password-dependent
+      - scenario:disk_imaging
+    related_concepts:
+      - Digital Evidence Chain of Custody
+    related_software:
+      - dd
+    icon: 🎯
+  - name: macOS Auto-Mount Prevention
+    type: method
+    description: >-
+      Verhinderung automatischen Mountens durch temporäres Deaktivieren des Disk
+      Arbitrators. Verfahren: 1) 'sudo launchctl unload
+      /System/Library/LaunchDaemons/com.apple.diskarbitrationd.plist' ausführen,
+      2) Datenträger anschließen (kein diskutil verfügbar), 3) Imaging
+      durchführen, 4) 'sudo launchctl load
+      /System/Library/LaunchDaemons/com.apple.diskarbitrationd.plist' zur
+      Reaktivierung. Alternative zu Hardware-Schreibblockern für forensisch
+      saubere Anbindung.
+    url: https://cloud.cc24.dev/f/32324
+    skillLevel: intermediate
+    domains:
+      - incident-response
+      - static-investigations
+    phases:
+      - data-collection
+    platforms:
+      - macOS
+    tags:
+      - write-blocker
+      - disk-arbitrator
+      - system-modification
+      - contamination-prevention
+      - sudo-required
+      - manual-mounting
+    related_concepts:
+      - Digital Evidence Chain of Custody
+    icon: 🚫
+  - name: macOS Property List Analysis
+    type: method
+    description: >-
+      Systematische Auswertung von Apple Property List Dateien (Plist) in XML-
+      und Binärformat für Konfigurationsdaten und Systemeinstellungen.
+      Konvertierung zwischen Formaten mit plutil-Tool, strukturierte Analyse des
+      binären Objektaufbaus mit 8-Byte-Header, variabler Objekttabelle,
+      Offset-Tabelle und 32-Byte-Trailer. Extraktion forensischer Artefakte aus
+      Systemkonfigurationen, Anwendungseinstellungen und Benutzeraktivitäten.
+    url: https://cloud.cc24.dev/f/32324
+    skillLevel: intermediate
+    domains:
+      - static-investigations
+      - incident-response
+      - mobile-forensics
+    phases:
+      - examination
+      - analysis
+    platforms:
+      - macOS
+    tags:
+      - plist-analysis
+      - binary-decode
+      - configuration-parsing
+      - system-metadata
+      - artifact-extraction
+      - format-conversion
+      - xml-parsing
+    icon: 📋
+    knowledgebase: true
+  - name: macOS Spotlight Forensic Analysis
+    type: method
+    description: >-
+      Tiefgehende Auswertung der Spotlight-Indexdatenbanken (.store-Dateien) für
+      umfassende Dateimetadaten und Aktivitätsnachweise. Extraktion von
+      Öffnungshäufigkeiten, letzten Zugriffsdaten und versteckten Metadaten die
+      im normalen Dateisystem nicht verfügbar sind. Fundorte:
+      /.Spotlight-V100/Store-V2/<UUID>/.store und
+      ~/Library/Metadata/CoreSpotlight/. Analyse erfordert spezielle
+      Python-Parser für proprietäres undokumentiertes Format.
+    url: https://cloud.cc24.dev/f/32324
+    skillLevel: advanced
+    domains:
+      - static-investigations
+      - incident-response
+    phases:
+      - examination
+      - analysis
+    platforms:
+      - macOS
+    tags:
+      - spotlight-analysis
+      - metadata-extraction
+      - timeline-analysis
+      - file-activity
+      - indexing-forensics
+      - database-parsing
+      - python-tools
+    related_concepts:
+      - Timeline Analysis & Event Correlation
+    icon: 🔍
+    knowledgebase: true
+  - name: macOS FSEvents Analysis
+    type: method
+    description: >-
+      Forensische Auswertung von FSEvents-Logs für historische
+      Dateisystemänderungen seit macOS 10.7. Fundort: /.fseventsd/xxxxxxxxxx
+      (gzip-komprimiert). Dateiname entspricht letzter Event-ID + 1.
+      Dekomprimierung und Parsing für chronologische Rekonstruktion von
+      Datei-/Ordner-Operationen. Besonderheit: alphabetische statt
+      chronologische Speicherung erfordert spezielle Parser für zeitbasierte
+      Analyse und Aktivitätsmuster-Erkennung.
+    url: https://cloud.cc24.dev/f/32324
+    skillLevel: intermediate
+    domains:
+      - static-investigations
+      - incident-response
+    phases:
+      - examination
+      - analysis
+    platforms:
+      - macOS
+    tags:
+      - fsevents-analysis
+      - filesystem-monitoring
+      - historical-analysis
+      - gzip-decompression
+      - timeline-reconstruction
+      - file-operations
+      - activity-tracking
+    related_concepts:
+      - Timeline Analysis & Event Correlation
+      - File Systems & Storage Forensics
+    icon: 📁
+    knowledgebase: true
+  - name: macOS Keychain Forensic Analysis
+    type: method
+    description: >-
+      Extraktion und Entschlüsselung von Passwörtern aus macOS Keychain-Dateien
+      (~/Library/Keychains/*) für Zugangsdaten-Recovery. Auf T2/M1-Systemen
+      Hardware-Bindung durch HEK-Schlüssel, Entschlüsselung nur mit bekanntem
+      Benutzerpasswort möglich. Neuere Versionen: Export-Funktion deaktiviert,
+      Alternative über Safari-Password-Manager (CSV-Export) oder
+      Python-Chainbreaker-Tools für Offline-Analyse.
+    url: https://cloud.cc24.dev/f/32324
+    skillLevel: advanced
+    domains:
+      - static-investigations
+      - incident-response
+      - fraud-investigation
+    phases:
+      - examination
+      - analysis
+    platforms:
+      - macOS
+    tags:
+      - keychain-analysis
+      - password-extraction
+      - hardware-encryption
+      - credential-recovery
+      - t2-m1-limitations
+      - python-tools
+      - access-restriction
+    related_concepts:
+      - Hash Functions & Digital Signatures
+    icon: 🔐
+    knowledgebase: true
+  - name: macOS Unified Log Analysis
+    type: method
+    description: >-
+      Dekodierung und Analyse des Apple Unified Logging (AUL) ab macOS 10.12 für
+      systemweite Event-Korrelation. Datenquellen: /var/db/diagnostics/Persist/
+      (tracev3-Dateien), /var/db/diagnostics/Special/ und /var/db/uuidtext.
+      Analyse mit 'log show', 'log collect', Predicate-Filter für gezielte
+      Suchen. Offline-Analyse erfordert Übertragung der Verzeichnisse auf
+      Analyse-Mac oder Virtualisierung des Zielsystems.
+    url: https://cloud.cc24.dev/f/32324
+    skillLevel: advanced
+    domains:
+      - incident-response
+      - static-investigations
+    phases:
+      - examination
+      - analysis
+    platforms:
+      - macOS
+    tags:
+      - unified-logging
+      - tracev3-parsing
+      - predicate-filters
+      - subsystem-analysis
+      - binary-log-format
+      - timeline-creation
+      - system-monitoring
+    related_concepts:
+      - Timeline Analysis & Event Correlation
+    icon: 📊
+    knowledgebase: true
+  - name: macOS DMG Image Mounting
+    type: method
+    description: >-
+      Forensisch sauberes Mounting von Mac Disk Images mit Write-Protection.
+      Verfahren: 1) DMG-Datei als 'geschützt' markieren (Schloss-Icon), 2)
+      'hdiutil attach -shadow' für Shadow-File-Erstellung, 3) 'mdutil -i on' für
+      Spotlight-Indexierung ohne Original-Änderung. Alternative: RAW/E01-Images
+      mit xmount zu DMG konvertieren. Ermöglicht native macOS-Tool-Nutzung und
+      Spotlight-Durchsuchbarkeit bei forensischer Integrität.
+    url: https://cloud.cc24.dev/f/32324
+    skillLevel: intermediate
+    domains:
+      - static-investigations
+      - incident-response
+    phases:
+      - examination
+      - analysis
+    platforms:
+      - macOS
+    tags:
+      - dmg-mounting
+      - write-protection
+      - shadow-files
+      - spotlight-indexing
+      - image-conversion
+      - forensic-imaging
+      - read-only-access
+    related_concepts:
+      - Digital Evidence Chain of Custody
+    related_software:
+      - OSFMount
+    icon: 💿
+  - name: macOS Time Machine Backup Analysis
+    type: method
+    description: >-
+      Forensische Auswertung von Time Machine Backups für historische
+      Systemzustände. Bis macOS 10.x: HFS+-Sparse-Bundle mit Hardlinks, ab
+      BigSur: APFS-Snapshots. Analyse-Befehle: 'tmutil listbackups', 'tmutil
+      listlocalsnapshots', 'tmutil destinationinfo'. Backup-Struktur: stündlich
+      (24h), täglich (1 Monat), wöchentlich (permanent). Auswertung der
+      backup_manifest.plist für Snapshot-IDs und Timeline-Rekonstruktion.
+    url: https://cloud.cc24.dev/f/32324
+    skillLevel: intermediate
+    domains:
+      - static-investigations
+      - incident-response
+    phases:
+      - examination
+      - analysis
+    platforms:
+      - macOS
+    tags:
+      - time-machine-analysis
+      - backup-forensics
+      - sparse-bundle
+      - apfs-snapshots
+      - hardlink-analysis
+      - historical-recovery
+      - manifest-parsing
+    related_concepts:
+      - Timeline Analysis & Event Correlation
+      - File Systems & Storage Forensics
+    icon: ⏰
+    knowledgebase: true
+  - name: iOS Backup Forensic Analysis
+    type: method
+    description: >-
+      Extraktion und Analyse von iTunes/iCloud-Backups für iOS-Geräte-Forensik.
+      Fundorte: Windows: /Users/[User]/AppData/Roaming/Apple
+      Computer/MobileSync/Backup/, macOS: ~/Library/Application
+      Support/MobileSync/Backup/. UDID-basierte Ordnerstruktur (SHA1 aus
+      Seriennummer/IMEI/MAC), GUID-benannte Dateien enthalten
+      iOS-Dateisystem-Inhalte. Analyse von Info.plist, Manifest.plist,
+      Status.plist für Backup-Metadaten.
+    url: https://cloud.cc24.dev/f/32324
+    skillLevel: intermediate
+    domains:
+      - mobile-forensics
+      - static-investigations
+    phases:
+      - examination
+      - analysis
+    platforms:
+      - macOS
+    tags:
+      - ios-backup-analysis
+      - udid-decoding
+      - manifest-parsing
+      - app-data-recovery
+      - backup-decryption
+      - mobile-artifacts
+      - itunes-backup
+    related_concepts:
+      - SQL
+      - Digital Evidence Chain of Custody
+    related_software:
+      - Cellebrite UFED
+    icon: 📱
+    knowledgebase: true
+  - name: macOS iCloud Artifact Analysis
+    type: method
+    description: >-
+      Systematische Auswertung von iCloud-Synchronisations-Artefakten im lokalen
+      Dateisystem. Fundorte: ~/Library/Application Support/iCloud/Accounts
+      (iCloud-ID), ~/Library/Mobile Documents/ (synchronisierte Dateien),
+      ~/Library/SyncedPreferences/ (App-Einstellungen), ~/Library/Application
+      Support/CloudDocs/ (client.db/server.db). Extraktion der account.1-Datei
+      und .DS_Store-Analyse für Cloud-Aktivitätsnachweise ohne direkten
+      iCloud-Zugriff.
+    url: https://cloud.cc24.dev/f/32324
+    skillLevel: advanced
+    domains:
+      - cloud-forensics
+      - static-investigations
+    phases:
+      - examination
+      - analysis
+    platforms:
+      - macOS
+    tags:
+      - icloud-forensics
+      - cloud-synchronization
+      - mobile-documents
+      - token-extraction
+      - sync-analysis
+      - cloud-artifacts
+      - metadata-analysis
+    related_concepts:
+      - Hash Functions & Digital Signatures
+    icon: ☁️
+    knowledgebase: true
+  - name: macOS Communication App Analysis
+    type: method
+    description: >-
+      Forensische Untersuchung nativer macOS Kommunikations-Apps für
+      Nachrichtenverlauf. Messages: chat.db-SQLite-Datenbank (Tabellen: chat,
+      messages, handle, attachments), Attachments-Verzeichnis. Mail: Envelope
+      Index-Datenbank, .emlx-Dateien, Accounts-SQLite. Kontakte:
+      AddressBook-v22.abcddb, MailRecents-v4.abcdmr. FaceTime:
+      Plist-Konfigurationen mit Anruflisten. Korrelation zwischen Apps und
+      iOS-Synchronisation.
+    url: https://cloud.cc24.dev/f/32324
+    skillLevel: intermediate
+    domains:
+      - static-investigations
+      - fraud-investigation
+      - incident-response
+    phases:
+      - examination
+      - analysis
+    platforms:
+      - macOS
+    tags:
+      - communication-analysis
+      - chat-db-analysis
+      - message-recovery
+      - attachment-extraction
+      - contact-analysis
+      - ios-synchronization
+      - sqlite-parsing
+    related_concepts:
+      - SQL
+      - Timeline Analysis & Event Correlation
+    icon: 💬
+    knowledgebase: true
+  - name: Windows Passwort-Umgehung via Utilman-Ersetzung
+    type: method
+    description: >-
+      Boot mit Linux-Live-System (z.B. Kali Linux), Mount der
+      Windows-Partition,  Backup von C:\Windows\System32\Utilman.exe erstellen,
+      Utilman.exe durch  cmd.exe überschreiben, Windows-Neustart, Klick auf
+      Erleichterte Bedienung  (Uhr-Symbol) öffnet CMD mit Systemrechten, "net
+      user USERNAME NEUES_PASSWORT"  eingeben für Passwort-Reset. Nach Anmeldung
+      Original-Utilman.exe  wiederherstellen. Funktioniert bei allen
+      Windows-Versionen ohne BitLocker.
+    url: https://cloud.cc24.dev/f/32333
+    skillLevel: intermediate
+    domains:
+      - incident-response
+      - static-investigations
+    phases:
+      - data-collection
+    platforms:
+      - Windows
+    tags:
+      - password-bypass
+      - system-access
+      - utilman-replacement
+      - live-boot
+      - administrator-rights
+      - net-user-command
+      - linux-tools
+    related_concepts:
+      - Digital Evidence Chain of Custody
+    related_software:
+      - Kali Linux
+    icon: 🔓
+  - name: RDP Cache Analyse
+    type: method
+    description: >-
+      Systematische Extraktion von RDP-Cache-Dateien aus 
+      C:\Users\XXX\AppData\Local\Microsoft\Terminal Server Client\Cache für 
+      Lateral-Movement-Nachweis. 64x64-Bitmap-Fragmente mit speziellen Tools 
+      extrahieren und zu Bildschirm-Screenshots rekonstruieren.
+      Registry-Analyse  von MRU-Einträgen unter
+      HKEY_CURRENT_USER\Software\Microsoft\Terminal  Server Client\Default für
+      RDP-Server-Historie und UsernameHint-Extraktion  aus \Servers-Schlüssel.
+      Korrelation mit Event-Logs für vollständige  RDP-Timeline.
+    url: https://cloud.cc24.dev/f/32333
+    skillLevel: advanced
+    domains:
+      - incident-response
+      - network-forensics
+      - malware-analysis
+    phases:
+      - examination
+      - analysis
+    platforms:
+      - Windows
+    tags:
+      - rdp-cache
+      - lateral-movement
+      - terminal-server
+      - bitmap-analysis
+      - mru-analysis
+      - apt-investigation
+      - network-intrusion
+      - cache-forensics
+    related_concepts:
+      - Timeline Analysis & Event Correlation
+    related_software:
+      - RegRipper
+      - Eric Zimmerman Tools
+      - bmc-tools
+    icon: 🖥️
+    knowledgebase: true
+  - name: Windows Event Log Forensische Analyse
+    type: method
+    description: >-
+      Dekodierung binärer EVT/EVTX-Logs durch Korrelation mit Message-Table-
+      Ressourcen in System-DLLs. Event-ID-Mapping über Registry-Einträge in 
+      HKLM\System\CurrentControlSet\Services\EventLog für Template-Zuordnung. 
+      Wichtige Event-IDs: 4624/4625 (Logon Success/Fail), 4778/4779 (RDP 
+      Connect/Disconnect), 7034-7045 (Service-Events). Timeline-Erstellung  mit
+      Plaso, XML-Export für strukturierte Analyse, Korrelation zwischen 
+      Security.evtx, System.evtx und Application.evtx für vollständige 
+      Systemaktivitäts-Rekonstruktion.
+    url: https://cloud.cc24.dev/f/32333
+    skillLevel: intermediate
+    domains:
+      - incident-response
+      - static-investigations
+      - network-forensics
+    phases:
+      - examination
+      - analysis
+    platforms:
+      - Windows
+    tags:
+      - event-logs
+      - evt-analysis
+      - evtx-analysis
+      - timeline-analysis
+      - logon-analysis
+      - rdp-sessions
+      - service-analysis
+      - message-tables
+      - xml-parsing
+    related_concepts:
+      - Timeline Analysis & Event Correlation
+    related_software:
+      - Plaso (log2timeline)
+      - Eric Zimmerman Tools
+      - Timesketch
+    icon: 📊
+    knowledgebase: true
+  - name: USB-Gerät Analyse unter Windows
+    type: method
+    description: >-
+      Registry-Extraktion aus SYSTEM\CurrentControlSet\Enum\USBSTOR für 
+      Hersteller/Produkt/Version-Identifikation und Unique-Serial-Numbers  (ohne
+      & im 2. Zeichen). First/Last-Connection-Zeiten aus Properties 
+      0064/0066/0067-Registry-Werten. Setupapi.dev.log-Analyse für Initial-
+      Installation-Timestamps. Volume-Serial-Number-Korrelation zwischen 
+      ENDMgmt-Registry und LNK-Dateien für Device-File-Zuordnung. 
+      MountedDevices-Registry für Laufwerksbuchstaben-Historie.
+    url: https://cloud.cc24.dev/f/32333
+    skillLevel: intermediate
+    domains:
+      - static-investigations
+      - incident-response
+      - fraud-investigation
+    phases:
+      - examination
+      - analysis
+    platforms:
+      - Windows
+    tags:
+      - usb-forensics
+      - registry-analysis
+      - pnp-logs
+      - volume-serial
+      - mounted-devices
+      - setupapi-analysis
+      - device-identification
+      - connection-timeline
+      - lnk-correlation
+    related_concepts:
+      - Timeline Analysis & Event Correlation
+      - Digital Evidence Chain of Custody
+    related_software:
+      - RegRipper
+      - Eric Zimmerman Tools
+      - USBDeview
+    icon: 🔌
+    knowledgebase: true
+  - name: Windows Registry Tiefenanalyse
+    type: method
+    description: >-
+      REGF/CREG-Format-Parsing von Registry-Hives (SYSTEM, SOFTWARE, SAM, 
+      SECURITY, NTUSER.DAT) für Systemkonfiguration und User-Activity. 
+      LastWrite-Timestamp-Analyse für Key-Änderungszeiten (Nanosekunden seit 
+      1601). Autorun-Location-Enumeration in Run/RunOnce-Schlüsseln für 
+      Persistenz-Mechanismen. Network-Signatures für WLAN-Historie, MRU-Listen 
+      für Recent-Activities, UserAssist-ROT13-Dekodierung für GUI-Starts, 
+      ShellBags für Folder-Access, 64-Bit-Support über Wow6432Node-Analyse.
+    url: https://cloud.cc24.dev/f/32333
+    skillLevel: intermediate
+    domains:
+      - static-investigations
+      - incident-response
+      - malware-analysis
+    phases:
+      - examination
+      - analysis
+    platforms:
+      - Windows
+    tags:
+      - registry-analysis
+      - hive-parsing
+      - lastwrite-timestamps
+      - autorun-detection
+      - mru-analysis
+      - userassist-decoding
+      - shellbags-analysis
+      - wow6432node
+      - rot13-decoding
+      - persistence-analysis
+    related_concepts:
+      - Timeline Analysis & Event Correlation
+    related_software:
+      - RegRipper
+      - Eric Zimmerman Tools
+      - Registry Explorer
+    icon: 📜
+    knowledgebase: true
+  - name: Volume Shadow Copy Forensische Analyse
+    type: method
+    description: >-
+      VSS-Snapshot-Auflistung mit "vssadmin list shadows /for=C:", Zugriff  via
+      mklink-Verknüpfung: "mklink /d c:\vss-test 
+      \\?\GLOBALROOT\Device\HarddiskVolumeShadowCopy4\". Imaging mit dd:  "dd
+      if=\\.\HarddiskVolumeShadowCopy4 of=shadowcopy.dd". X-Ways-Integration 
+      über Dateiüberblick erweitern → gründliche
+      Dateisystem-Datenstruktur-Suche  → SC-Attribut-Filterung für
+      Shadow-Copy-Dateien. Copy-on-Write-Prinzip:  nur Änderungen seit letztem
+      Snapshot gespeichert. Gelöschte Dateien  bleiben in Snapshots erhalten.
+    url: https://cloud.cc24.dev/f/32333
+    skillLevel: intermediate
+    domains:
+      - static-investigations
+      - incident-response
+      - fraud-investigation
+    phases:
+      - examination
+      - analysis
+    platforms:
+      - Windows
+    tags:
+      - shadow-copy-analysis
+      - vss-forensics
+      - snapshot-recovery
+      - vssadmin-commands
+      - mklink-access
+      - copy-on-write
+      - historical-analysis
+      - deleted-file-recovery
+      - x-ways-integration
+      - sc-filtering
+    related_concepts:
+      - Timeline Analysis & Event Correlation
+      - File Systems & Storage Forensics
+    related_software:
+      - ShadowExplorer
+      - X-Ways Forensics
+      - dd
+    icon: 📸
+  - name: Windows Netzwerk-Forensik mit PowerShell
+    type: method
+    description: >-
+      Live-System-Netzwerk-Sammlung: Get-NetAdapter für Interface-Liste, 
+      Get-NetIPAddress für IP-Konfiguration, Get-NetTCPConnection für aktive 
+      Verbindungen, Get-NetUDPEndpoint für UDP-Ports, Get-NetNeighbor für 
+      ARP-Tabelle, Get-NetRoute für Routing-Informationen. 
+      Get-NetAdapterStatistics für Traffic-Volumina,  Get-NetAdapterHardwareInfo
+      für Hardware-Details. Batch-Sammlung mit  | Export-Csv für strukturierte
+      Ausgabe. Korrelation mit Registry-Daten  unter NetworkList für historische
+      Profile.
+    url: https://cloud.cc24.dev/f/32333
+    skillLevel: intermediate
+    domains:
+      - incident-response
+      - network-forensics
+    phases:
+      - data-collection
+      - examination
+    platforms:
+      - Windows
+    tags:
+      - powershell-forensics
+      - network-analysis
+      - live-analysis
+      - netadapter-cmdlets
+      - tcp-connections
+      - routing-analysis
+      - neighbor-discovery
+      - traffic-statistics
+      - cmdlet-scripting
+    related_concepts:
+      - Network Protocols & Packet Analysis
+    related_software:
+      - Wireshark
+    icon: 🌐
+  - name: UserAssist Forensische Analyse
+    type: method
+    description: >-
+      ROT13-Dekodierung von NTUSER.DAT\Software\Microsoft\Windows\
+      CurrentVersion\Explorer\UserAssist\{GUID}\Count für
+      Desktop-GUI-Programme.  GUID-Kategorien: CEBFF5CD (Executable), F4E57C4B
+      (Shortcut), 75048700  (Active Desktop-XP). Jeder Eintrag enthält
+      Ausführungs-Count und  Last-Execution-Time. ROT13-Dekodierung: A→N, B→O
+      etc. Automated-Tools  für Batch-Dekodierung verfügbar. Registry-Explorer
+      zeigt Last-Write-Time  des UserAssist-Keys für Timeline-Korrelation.
+    url: https://cloud.cc24.dev/f/32333
+    skillLevel: intermediate
+    domains:
+      - static-investigations
+      - incident-response
+    phases:
+      - examination
+      - analysis
+    platforms:
+      - Windows
+    tags:
+      - userassist-analysis
+      - rot13-decoding
+      - gui-tracking
+      - execution-frequency
+      - registry-analysis
+      - desktop-programs
+      - timeline-analysis
+      - ntuser-dat
+      - guid-analysis
+    related_concepts:
+      - Timeline Analysis & Event Correlation
+    related_software:
+      - RegRipper
+      - Eric Zimmerman Tools
+      - UserAssist GUI
+    icon: 🖱️
+  - name: Shell Bags Analyse
+    type: method
+    description: >-
+      Ordnerzugriffs-Historie aus USRCLASS.DAT\Local Settings\Software\
+      Microsoft\Windows\Shell\Bags (Explorer) und NTUSER.DAT\Software\
+      Microsoft\Windows\Shell\BagMRU (Desktop). Jeder Bag enthält 
+      Folder-Settings und Zugriffs-Timestamps. BagMRU zeigt chronologische 
+      MRU-Liste mit letzten Ordner-Öffnungen. Nachweis gelöschter Verzeichnisse 
+      durch Registry-Persistenz nach Folder-Deletion. Netzwerk-Share-Zugriffe 
+      und USB-Pfade werden ebenfalls geloggt.
+    url: https://cloud.cc24.dev/f/32333
+    skillLevel: intermediate
+    domains:
+      - static-investigations
+      - incident-response
+      - fraud-investigation
+    phases:
+      - examination
+      - analysis
+    platforms:
+      - Windows
+    tags:
+      - shellbags-analysis
+      - folder-access
+      - network-shares
+      - deleted-directories
+      - usrclass-dat
+      - ntuser-dat
+      - explorer-forensics
+      - access-timeline
+      - data-exfiltration
+    related_concepts:
+      - Timeline Analysis & Event Correlation
+      - File Systems & Storage Forensics
+    related_software:
+      - Eric Zimmerman Tools
+      - RegRipper
+      - ShellBags Explorer
+    icon: 📁
+  - name: LNK-Dateien Forensische Analyse
+    type: method
+    description: >-
+      Shortcut-Analyse aus %USERPROFILE%\AppData\Roaming\Microsoft\Windows\
+      Recent\ für File-Access-Historie. Jede LNK-Datei enthält: Target-Path, 
+      Working-Directory, Creation/Modification/Access-Times der Zieldatei, 
+      Volume-Serial-Number, Network-Share-Info, Host-Name. LNK-Creation-Time  =
+      erste Datei-Öffnung, LNK-Modification-Time = letzte Öffnung. 
+      Besonderheit: LNK-Dateien für Netzwerk-Dateien bleiben auch nach 
+      Original-Löschung bestehen und zeigen Remote-Zugriffe.
+    url: https://cloud.cc24.dev/f/32333
+    skillLevel: beginner
+    domains:
+      - static-investigations
+      - incident-response
+      - fraud-investigation
+    phases:
+      - examination
+      - analysis
+    platforms:
+      - Windows
+    tags:
+      - lnk-analysis
+      - shortcut-forensics
+      - file-access-history
+      - volume-information
+      - network-shares
+      - target-metadata
+      - remote-files
+      - mac-timestamps
+      - serial-numbers
+    related_concepts:
+      - Timeline Analysis & Event Correlation
+      - File Systems & Storage Forensics
+    related_software:
+      - Eric Zimmerman Tools
+    icon: 🔗
+  - name: Prefetch-Dateien Forensische Analyse
+    type: method
+    description: >-
+      Programm-Execution-Nachweis durch C:\Windows\Prefetch\*.pf-Analyse. 
+      Dateiformat: EXENAME-HASH.pf mit Last-Execution-Time (Modification-Date), 
+      Run-Count, Referenced-Files/Directories. Creation-Time = erste
+      Ausführung.  Kapazitäten: 128 Files (XP/7), 1024 Files (Win8+). Hash
+      basiert auf  Executable-Path, nicht Content - Path-Änderung erzeugt neue
+      PF-Datei.  Prefetch-Status: Registry
+      HKLM\SYSTEM\CurrentControlSet\Control\ Session Manager\Memory
+      Management\PrefetchParameters.
+    url: https://cloud.cc24.dev/f/32333
+    skillLevel: intermediate
+    domains:
+      - incident-response
+      - malware-analysis
+      - static-investigations
+    phases:
+      - examination
+      - analysis
+    platforms:
+      - Windows
+    tags:
+      - prefetch-analysis
+      - program-execution
+      - execution-frequency
+      - last-execution-time
+      - file-handles
+      - malware-evidence
+      - performance-cache
+      - hash-calculation
+      - capacity-limits
+    related_concepts:
+      - Timeline Analysis & Event Correlation
+    related_software:
+      - Eric Zimmerman Tools
+    icon: ⚡
+  - name: Jump Lists Forensische Analyse
+    type: method
+    description: >-
+      Windows 7+ Taskbar-Recent-Items aus %USERPROFILE%\AppData\Roaming\
+      Microsoft\Windows\Recent\AutomaticDestinations\*.automaticDestinations-ms. 
+      AppID-basierte Dateinamen (z.B. 1bc392b3d42b9fb2.automaticDestinations-ms 
+      für Notepad). Jede Datei enthält LNK-Streams für Recent-Documents. 
+      Creation-Time = erste App-Nutzung, Modification-Time = letztes 
+      Document-Opening. Internal-LNK-Parsing zeigt accessed Files mit 
+      Timestamps und Paths.
+    url: https://cloud.cc24.dev/f/32333
+    skillLevel: intermediate
+    domains:
+      - static-investigations
+      - incident-response
+      - fraud-investigation
+    phases:
+      - examination
+      - analysis
+    platforms:
+      - Windows
+    tags:
+      - jump-lists
+      - taskbar-forensics
+      - automatic-destinations
+      - appid-analysis
+      - lnk-correlation
+      - user-workflow
+      - document-access
+      - chronological-analysis
+      - windows7-plus
+    related_concepts:
+      - Timeline Analysis & Event Correlation
+    related_software:
+      - Eric Zimmerman Tools
+    icon: 📋
+  - name: Thumbcache Analyse
+    type: method
+    description: >-
+      Thumbnail-Extraktion aus %USERPROFILE%\AppData\Local\Microsoft\Windows\
+      Explorer\thumbcache_*.db (Vista+) für Image-Viewing-Evidence. 
+      ThumbnailCacheID-Korrelation mit Windows.edb SystemIndex_0A-Tabelle  für
+      Original-Filename-Recovery. Thumbcache-Größen: 32, 96, 256, 1024  Pixel.
+      XP: thumbs.db pro Verzeichnis mit direkten Dateinamen.  Netzwerk-Shares
+      erzeugen weiterhin thumbs.db-Dateien auch unter Win10.  Thumbnails bleiben
+      nach Original-File-Deletion erhalten.
+    url: https://cloud.cc24.dev/f/32333
+    skillLevel: intermediate
+    domains:
+      - static-investigations
+      - fraud-investigation
+      - mobile-forensics
+    phases:
+      - examination
+      - analysis
+    platforms:
+      - Windows
+    tags:
+      - thumbcache-analysis
+      - thumbnail-forensics
+      - image-viewing
+      - thumbnailcacheid
+      - windows-edb
+      - systemindex-correlation
+      - deleted-images
+      - csam-investigation
+      - content-awareness
+      - thumbs-db
+    related_concepts:
+      - File Systems & Storage Forensics
+    related_software:
+      - Thumbcache Viewer
+    icon: 🖼️
+  - name: Windows Zone Identifier Analyse
+    type: method
+    description: >-
+      NTFS-Alternate-Data-Stream-Analyse für Download-Attribution: 
+      filename:Zone.Identifier mit ZoneId-Werten: 0=Local, 1=Intranet, 
+      2=Trusted Sites, 3=Internet, 4=Restricted Sites. Browser-Downloads 
+      erhalten automatisch Zone.Identifier-Markierung. Analyse mit  "dir /R"
+      oder "more filename:Zone.Identifier". Streams-Tool zeigt  alle ADS eines
+      Verzeichnisses. Registry-Policies können  Zone-Marking deaktivieren.
+    url: https://cloud.cc24.dev/f/32333
+    skillLevel: intermediate
+    domains:
+      - incident-response
+      - malware-analysis
+      - static-investigations
+    phases:
+      - examination
+      - analysis
+    platforms:
+      - Windows
+    tags:
+      - zone-identifier
+      - alternate-data-streams
+      - download-attribution
+      - security-zones
+      - ntfs-forensics
+      - browser-markings
+      - malware-sources
+      - web-attacks
+      - origin-tracking
+    related_concepts:
+      - File Systems & Storage Forensics
+    related_software:
+      - Strings
+      - Streams
+    icon: 🌐
+  - name: Outlook Attachments Forensische Analyse
+    type: method
+    description: >-
+      OLK-Cache-Analyse aus %USERPROFILE%\AppData\Local\Microsoft\Windows\
+      INetCache\Content.Outlook\[Random-GUID]\ für temporäre Attachments. 
+      Registry-Pfad-Ermittlung: HKCU\Software\Microsoft\Office\{VERSION}\
+      Outlook\Security\OutlookSecureTempFolder. Versions-Mapping: 12.0=2007, 
+      14.0=2010, 15.0=2013, 16.0=365. Opened/Deleted-Attachments bleiben  im
+      Cache bis Outlook-Restart. Filename-Original-Names meist erhalten. 
+      Integration mit PST-Analysis für E-Mail-Context-Correlation.
+    url: https://cloud.cc24.dev/f/32333
+    skillLevel: beginner
+    domains:
+      - static-investigations
+      - incident-response
+      - fraud-investigation
+    phases:
+      - examination
+      - analysis
+    platforms:
+      - Windows
+    tags:
+      - outlook-forensics
+      - email-attachments
+      - olk-cache
+      - inetcache-analysis
+      - registry-paths
+      - temporary-files
+      - attachment-timeline
+      - pst-integration
+      - email-investigation
+    related_concepts:
+      - Timeline Analysis & Event Correlation
+    related_software:
+      - libpff/pffexport
+    icon: 📧
+  - name: Office Files MRU Forensische Analyse
+    type: method
+    description: >-
+      Microsoft Office Recent-Files aus NTUSER.DAT\Software\Microsoft\
+      Office\{VERSION}\UserMRU\LiveID_####\FileMRU. Versions-Codes:  10.0=XP,
+      11.0=2003, 12.0=2007, 14.0=2010, 15.0=365, 16.0=365.  LiveID-Integration
+      zeigt Cloud-Account-Binding. MRU-Liste-Analyse  für chronologische
+      Dokument-Zugriffe mit Last-Access-Timestamps.  Application-spezifische
+      Unterschlüssel für Word, Excel, PowerPoint.  Integration mit
+      Recent-Folders für vollständige Office-Usage-Timeline.
+    url: https://cloud.cc24.dev/f/32333
+    skillLevel: beginner
+    domains:
+      - static-investigations
+      - incident-response
+      - fraud-investigation
+    phases:
+      - examination
+      - analysis
+    platforms:
+      - Windows
+    tags:
+      - office-mru
+      - recent-documents
+      - registry-analysis
+      - version-specific
+      - liveid-integration
+      - filemru-analysis
+      - document-access
+      - productivity-patterns
+      - cloud-accounts
+    related_concepts:
+      - Timeline Analysis & Event Correlation
+    related_software:
+      - RegRipper
+      - Eric Zimmerman Tools
+    icon: 📄
+  - name: Recent Dateien Registry-Analyse
+    type: method
+    description: >-
+      RecentDocs-Analyse aus NTUSER.DAT\Software\Microsoft\Windows\
+      CurrentVersion\Explorer\RecentDocs für 150 zuletzt geöffnete 
+      Files/Folders. MRU-Binary-Liste zeigt chronologische Reihenfolge 
+      (jüngste=0). Extension-Unterschlüssel (*.pdf, *.docx) für 
+      Filetype-spezifische Historie. Folder-Unterschlüssel für 
+      Directory-Access. LastWrite-Time des Keys = letzte Datei-Öffnung  dieser
+      Extension. Binary-Data enthält Unicode-Filename-Strings.
+    url: https://cloud.cc24.dev/f/32333
+    skillLevel: beginner
+    domains:
+      - static-investigations
+      - incident-response
+      - fraud-investigation
+    phases:
+      - examination
+      - analysis
+    platforms:
+      - Windows
+    tags:
+      - recent-docs
+      - registry-analysis
+      - mru-lists
+      - file-access-history
+      - extension-tracking
+      - folder-access
+      - lastwrite-correlation
+      - user-activity
+      - chronological-analysis
+    related_concepts:
+      - Timeline Analysis & Event Correlation
+    related_software:
+      - RegRipper
+      - Eric Zimmerman Tools
+    icon: 📂
+  - name: Windows Anmeldeverfahren Forensische Analyse
+    type: method
+    description: >-
+      Authentication-Mechanismus-Analyse: NTLM Challenge-Response vs.  Kerberos
+      Ticket-Granting für Domain-Environments. Event-Log-Analysis  für
+      Logon-Types: 2=Interactive Console, 3=Network, 4=Batch,  5=Service,
+      7=Unlock, 8=Network Cleartext, 9=New Credentials,  10=Remote Interactive
+      (RDP), 11=Cached Interactive. Registry-Analysis  für
+      Cached-Domain-Credentials unter SECURITY\Cache und Authentication-
+      Packages in LSA. Smartcard/Biometric-Evidence in specialized Event-Logs.
+    url: https://cloud.cc24.dev/f/32333
+    skillLevel: advanced
+    domains:
+      - incident-response
+      - static-investigations
+      - network-forensics
+    phases:
+      - examination
+      - analysis
+    platforms:
+      - Windows
+    tags:
+      - authentication-analysis
+      - logon-types
+      - interactive-logon
+      - smartcard-analysis
+      - biometric-analysis
+      - network-logon
+      - ntlm-analysis
+      - kerberos-analysis
+      - cached-credentials
+      - event-correlation
+    related_concepts:
+      - Timeline Analysis & Event Correlation
+      - Network Protocols & Packet Analysis
+    icon: 🔐
+    knowledgebase: true
+  - name: OpenSave MRU Forensische Analyse
+    type: method
+    description: >-
+      Shell-Dialog-basierte File-History aus NTUSER.DAT\Software\Microsoft\
+      Windows\CurrentVersion\Explorer\ComDlg32\OpenSaveMRU (XP) bzw. 
+      OpenSavePIDlMRU (Win7+). "*"-Subkey = alle Dateien, Extension-Subkeys 
+      (*.txt, *.jpg) = filetype-spezifisch. Binary-MRU-Data enthält  File-Paths
+      und Shell-Item-IDs. LastWrite-Time = letzte Dialog-Usage  dieser
+      Extension. Covers alle Anwendungen die Standard-Windows- File-Dialogs
+      verwenden (nicht Browser-Downloads).
+    url: https://cloud.cc24.dev/f/32333
+    skillLevel: intermediate
+    domains:
+      - static-investigations
+      - incident-response
+      - fraud-investigation
+    phases:
+      - examination
+      - analysis
+    platforms:
+      - Windows
+    tags:
+      - opensave-mru
+      - shell-dialogs
+      - file-access-tracking
+      - comdlg32-analysis
+      - extension-specific
+      - registry-timestamps
+      - user-interaction
+      - dialog-boxes
+      - application-forensics
+    related_concepts:
+      - Timeline Analysis & Event Correlation
+    related_software:
+      - RegRipper
+      - Eric Zimmerman Tools
+    icon: 💾
+  - name: Last-Visited MRU Forensische Analyse
+    type: method
+    description: >-
+      Application-zu-Path-Korrelation aus NTUSER.DAT\Software\Microsoft\
+      Windows\CurrentVersion\Explorer\ComDlg32\LastVisitedMRU (XP) bzw. 
+      LastVisitedPidlMRU (Win7+). Binary-Data-Format: Executable-Name + 
+      Opened-Path-String. Beispiel: notepad.exe → C:\Users\Rob\Desktop  zeigt
+      von wo Notepad gestartet wurde. MRU-Reihenfolge = chronologische 
+      App-Launches mit File-Dialog-Usage. Malware-Launch-Directory- Attribution
+      für Portable-Executables oder Removable-Media-Starts.
+    url: https://cloud.cc24.dev/f/32333
+    skillLevel: intermediate
+    domains:
+      - incident-response
+      - malware-analysis
+      - static-investigations
+    phases:
+      - examination
+      - analysis
+    platforms:
+      - Windows
+    tags:
+      - lastvisited-mru
+      - application-paths
+      - executable-tracking
+      - launch-directories
+      - malware-origins
+      - portable-apps
+      - execution-timeline
+      - path-correlation
+      - startup-analysis
+    related_concepts:
+      - Timeline Analysis & Event Correlation
+    related_software:
+      - RegRipper
+      - Eric Zimmerman Tools
+    icon: 🗂️
+  - name: IE History Laufwerkszugriffe Analyse
+    type: method
+    description: >-
+      Non-Browser-File-Access via IE-History in index.dat (IE6-9) bzw. 
+      WebCacheV*.dat (IE10+) unter %USERPROFILE%\AppData\Local\Microsoft\
+      Windows\WebCache\. file:///-URL-Entries zeigen lokale Dateizugriffe  und
+      UNC-Network-Share-Access auch ohne Browser-Usage. URL-Format: 
+      file:///C:/path/file.ext oder file://server/share/file. 
+      Timestamp-Analysis für File-Access-Timeline unabhängig von 
+      Explorer-Recent-Lists. WebCacheView-Tool für structured Database-Export.
+    url: https://cloud.cc24.dev/f/32333
+    skillLevel: intermediate
+    domains:
+      - static-investigations
+      - incident-response
+      - network-forensics
+    phases:
+      - examination
+      - analysis
+    platforms:
+      - Windows
+    tags:
+      - ie-history
+      - file-protocol
+      - local-file-access
+      - unc-shares
+      - index-dat
+      - webcache-analysis
+      - non-browser-access
+      - timeline-reconstruction
+      - network-shares
+      - remote-files
+    related_concepts:
+      - Timeline Analysis & Event Correlation
+      - Network Protocols & Packet Analysis
+    related_software:
+      - WebCacheView
+    icon: 🌐
+  - name: bmc-tools
+    type: software
+    description: >-
+      Extrahiert RDP-Bitmap-Cache-Fragmente aus Windows Terminal Server Client
+      Cache für Remote-Desktop-Session-Rekonstruktion. Parst bcache*.bmc und
+      cache*.bin Dateien durch Python-Framework mit automatischer
+      Tile-Dekompression und Collage-Generation. Unterstützt alle RDP-Versionen
+      und Qualitätsstufen (bcache2/22/24). Besonders wertvoll: versteckte
+      Remote-Aktivitäten ohne Server-Logs rekonstruieren, KAPE-Integration für
+      Batch-Processing, Export in Standard-Bildformate.
+    skillLevel: intermediate
+    url: https://github.com/ANSSI-FR/bmc-tools
+    domains:
+      - incident-response
+      - network-forensics
+      - static-investigations
+    phases:
+      - examination
+      - analysis
+    platforms:
+      - Windows
+      - Linux
+      - macOS
+    tags:
+      - cli
+      - rdp-analysis
+      - bitmap-extraction
+      - remote-session
+      - python-tool
+      - collage-generation
+      - lateral-movement
+      - cache-forensics
+      - terminal-server
+      - scenario:remote_access
+    related_concepts:
+      - Timeline Analysis & Event Correlation
+    related_software:
+      - RdpCacheStitcher
+    icon: 🖥️
+    license: GPL-3.0
+    accessType: download
+  - name: USBDeview
+    type: software
+    description: >-
+      Analysiert USB-Geräte-Historie für Datenträger-Forensik und
+      Incident-Response durch detaillierte Device-Information-Extraktion. Zeigt
+      aktuell verbundene und früher genutzte USB-Devices mit Hersteller-Details,
+      Seriennummern, Installation-Timestamps. Speed-Test-Feature für
+      USB-Performance-Analyse, Remote-System-Unterstützung für
+      Netzwerk-Forensik. Besonders wertvoll: USB-Autorun-Detection,
+      Device-Management und Export in strukturierte Formate für
+      Timeline-Korrelation.
+    skillLevel: beginner
+    url: https://www.nirsoft.net/utils/usb_devices_view.html
+    domains:
+      - static-investigations
+      - incident-response
+      - fraud-investigation
+    phases:
+      - examination
+      - analysis
+    platforms:
+      - Windows
+    tags:
+      - gui
+      - usb-forensics
+      - device-history
+      - serial-numbers
+      - installation-timeline
+      - speed-testing
+      - autorun-detection
+      - remote-analysis
+      - csv-export
+      - scenario:file_recovery
+    related_concepts:
+      - Timeline Analysis & Event Correlation
+      - Digital Evidence Chain of Custody
+    related_software:
+      - USBLogView
+    icon: 🔌
+    license: Freeware
+    accessType: download
+  - name: ShellBags Explorer
+    type: software
+    description: >-
+      GUI-basierte Shellbags-Analyse für Windows-Folder-Access-Rekonstruktion
+      durch Registry-Hive-Parsing von USRCLASS.DAT und NTUSER.DAT. Visualisiert
+      Verzeichnisstrukturen mit First/Last-Interaction-Timestamps, zeigt
+      gelöschte Ordner-Zugriffe und externe Device-Verbindungen. Besonders
+      wertvoll: Timeline-Export für Korrelations-Analyse, Batch-Processing
+      mehrerer Hives, detaillierte Shellbag-Properties für forensische
+      Dokumentation.
+    skillLevel: intermediate
+    url: https://ericzimmerman.github.io/
+    domains:
+      - static-investigations
+      - incident-response
+      - fraud-investigation
+    phases:
+      - examination
+      - analysis
+    platforms:
+      - Windows
+    tags:
+      - gui
+      - shellbags-analysis
+      - folder-access
+      - registry-analysis
+      - timeline-creation
+      - deleted-directories
+      - external-devices
+      - batch-processing
+      - eric-zimmerman
+      - scenario:file_recovery
+    related_concepts:
+      - Timeline Analysis & Event Correlation
+      - File Systems & Storage Forensics
+    related_software:
+      - Eric Zimmerman Tools
+      - Registry Explorer
+    icon: 📁
+    license: MIT
+    accessType: download
+  - name: UserAssist GUI
+    type: software
+    description: >-
+      Dekodiert ROT13-verschlüsselte UserAssist-Registry-Einträge für
+      GUI-Program-Execution- Tracking unter Windows. Zeigt Program-Pfade,
+      Ausführungsanzahl und Last-Execution- Times für Desktop-Programme und
+      Shortcuts. Besonders wertvoll: unterscheidet zwischen direkter
+      .exe-Ausführung und Shortcut-Starts, exportiert Daten für
+      Timeline-Analyse, erkennt Malware-Execution-Patterns automatisch.
+    skillLevel: beginner
+    url: https://blog.didierstevens.com/programs/userassist/
+    domains:
+      - static-investigations
+      - incident-response
+      - malware-analysis
+    phases:
+      - examination
+      - analysis
+    platforms:
+      - Windows
+    tags:
+      - gui
+      - userassist-analysis
+      - rot13-decoding
+      - program-execution
+      - registry-analysis
+      - execution-frequency
+      - shortcut-tracking
+      - malware-detection
+      - didier-stevens
+      - scenario:persistence
+    related_concepts:
+      - Timeline Analysis & Event Correlation
+    related_software:
+      - Registry Explorer
+      - Eric Zimmerman Tools
+    icon: 🖱️
+    license: Freeware
+    accessType: download
+  - name: Registry Explorer
+    type: software
+    description: >-
+      Erweiterte Registry-Analyse-Plattform mit Multi-Hive-Support und
+      forensischen Plugins für Windows-Artefakt-Extraktion. Lädt
+      Transaction-Logs für Live-Registry- Reconstruction, zeigt gelöschte
+      Keys/Values, bietet forensische Bookmarks für kritische
+      Registry-Locations. Besonders wertvoll: Plugin-System für automatisierte
+      Artefakt-Parsing, Locked-File-Handling, Search-Funktionen und
+      Export-Capabilities für strukturierte Analyse-Workflows.
+    skillLevel: intermediate
+    url: https://ericzimmerman.github.io/
+    domains:
+      - static-investigations
+      - incident-response
+      - malware-analysis
+    phases:
+      - examination
+      - analysis
+    platforms:
+      - Windows
+    tags:
+      - gui
+      - registry-analysis
+      - multi-hive-support
+      - plugin-system
+      - transaction-logs
+      - deleted-keys
+      - forensic-bookmarks
+      - locked-files
+      - search-capabilities
+      - eric-zimmerman
+    related_concepts:
+      - Timeline Analysis & Event Correlation
+    related_software:
+      - Eric Zimmerman Tools
+      - RegRipper
+    icon: 📜
+    license: MIT
+    accessType: download
+  - name: Streams
+    type: software
+    description: >-
+      Sysinternals-Tool detektiert NTFS-Alternate-Data-Streams für versteckte
+      Malware- und Daten-Identifikation. Zeigt alle Streams pro
+      Datei/Verzeichnis mit Größen- Informationen, unterstützt rekursive
+      Verzeichnis-Scans. Besonders wertvoll: Malware-Hiding-Detection in ADS,
+      Zone.Identifier-Analyse für Download-Attribution, Command-Line-Integration
+      für Batch-Processing und Security-Audits.
+    skillLevel: beginner
+    url: https://learn.microsoft.com/en-us/sysinternals/downloads/streams
+    domains:
+      - incident-response
+      - malware-analysis
+      - static-investigations
+    phases:
+      - examination
+      - analysis
+    platforms:
+      - Windows
+    tags:
+      - cli
+      - alternate-data-streams
+      - ntfs-forensics
+      - malware-detection
+      - zone-identifier
+      - hidden-data
+      - sysinternals
+      - security-audit
+      - download-attribution
+      - recursive-scan
+    related_concepts:
+      - File Systems & Storage Forensics
+    related_software:
+      - AlternateStreamView
+    icon: 💾
+    license: Microsoft
+    accessType: download
+  - name: WebCacheView
+    type: software
+    description: >-
+      Analysiert Internet Explorer WebCache-Datenbanken (WebCacheV*.dat) für
+      Browser- Forensik ab IE10/Edge. Extrahiert Historie, Downloads, Cookies
+      und Cache-Einträge aus ESE-Database-Format. Besonders wertvoll: lokale
+      Datei-Zugriffe über file:///- Protocol, InPrivate-Session-Detection,
+      Timeline-Export für Korrelations-Analyse und strukturierte Datenausgabe
+      für forensische Dokumentation.
+    skillLevel: beginner
+    url: https://www.nirsoft.net/utils/web_cache_view.html
+    domains:
+      - static-investigations
+      - incident-response
+      - fraud-investigation
+    phases:
+      - examination
+      - analysis
+    platforms:
+      - Windows
+    tags:
+      - gui
+      - web-cache-analysis
+      - internet-explorer
+      - edge-browser
+      - ese-database
+      - download-history
+      - cookies-analysis
+      - file-protocol
+      - inprivate-detection
+      - nirsoft
+    related_concepts:
+      - Timeline Analysis & Event Correlation
+    related_software:
+      - BrowsingHistoryView
+      - IECacheView
+    icon: 🌐
+    license: Freeware
+    accessType: download
+  - name: libpff/pffexport
+    type: software
+    description: >-
+      Open-Source-Bibliothek für PST/OST-Forensik mit detaillierter
+      Personal-Folder- File-Analyse und Deleted-Item-Recovery. Exportiert
+      E-Mails, Kontakte, Kalender aus Microsoft-Outlook-Dateien ohne
+      Outlook-Installation. Besonders wertvoll: Low-Level-MAPI-Property-Zugriff,
+      gefälschte E-Mail-Detection durch Metadaten- Analyse,
+      Cross-Platform-Support und Integration in forensische Toolchains.
+    skillLevel: advanced
+    url: https://github.com/libyal/libpff
+    domains:
+      - static-investigations
+      - fraud-investigation
+      - incident-response
+    phases:
+      - examination
+      - analysis
+    platforms:
+      - Windows
+      - Linux
+      - macOS
+    tags:
+      - cli
+      - pst-analysis
+      - ost-forensics
+      - email-forensics
+      - deleted-recovery
+      - mapi-properties
+      - outlook-forensics
+      - cross-platform
+      - libyal
+      - falsification-detection
+    related_concepts:
+      - Timeline Analysis & Event Correlation
+      - Digital Evidence Chain of Custody
+    related_software:
+      - Outlook Forensic Toolbox
+    icon: 📧
+    license: LGPL-3.0
+    accessType: download
+  - name: Linux Netzwerk-Konfiguration Analyse
+    type: method
+    description: >-
+      Systematische Auswertung von /etc/network/interfaces (Debian/Ubuntu), 
+      /etc/sysconfig/network (SUSE), /etc/NetworkManager (WLAN) für 
+      Netzwerk-Timeline-Reconstruction. Static/Dynamic-IP-Analysis, 
+      Gateway-Configuration, WLAN-SSID-History und Interface-Status-Tracking.
+    url: https://cloud.cc24.dev/f/32342
+    skillLevel: intermediate
+    domains:
+      - incident-response
+      - network-forensics
+    phases:
+      - examination
+      - analysis
+    tags:
+      - network-configuration
+      - interface-analysis
+      - wlan-analysis
+      - ip-configuration
+      - gateway-analysis
+    related_concepts:
+      - Network Protocols & Packet Analysis
+  - name: Linux File Access Timestamp Analyse
+    type: method
+    description: >-
+      Dateizugriffs-Rekonstruktion durch stat, ls -l, date -r für 
+      Access/Modify/Change-Time-Analysis. Filesystem-Metadaten-Extraction, 
+      Touch-Command-Detection, Root-Manipulation-Identification. Correlation 
+      mit System-Logs für vollständige File-Activity-Timeline.
+    url: https://cloud.cc24.dev/f/32342
+    skillLevel: beginner
+    domains:
+      - static-investigations
+      - incident-response
+    phases:
+      - examination
+      - analysis
+    tags:
+      - file-timestamps
+      - metadata-analysis
+      - access-time
+      - modification-time
+      - filesystem-analysis
+    related_concepts:
+      - File Systems & Storage Forensics
+      - Timeline Analysis & Event Correlation
+  - name: Linux Package Management Forensik
+    type: method
+    description: >-
+      APT/RPM-Paketmanager-Analyse für Software-Installation-Timeline und 
+      Integrity-Verification. Repository-Source-Analysis, Package-History-
+      Reconstruction, Dependency-Tracking, Malicious-Package-Detection durch 
+      Signature-Verification und Update-Pattern-Analysis.
+    url: https://cloud.cc24.dev/f/32342
+    skillLevel: intermediate
+    domains:
+      - incident-response
+      - malware-analysis
+    phases:
+      - examination
+      - analysis
+    tags:
+      - package-analysis
+      - software-timeline
+      - installation-history
+      - dependency-tracking
+      - integrity-verification
+    related_concepts:
+      - Hash Functions & Digital Signatures
+      - Timeline Analysis & Event Correlation
+  - name: Linux Namespace Container-Forensik
+    type: method
+    description: >-
+      Container-Isolation-Analysis durch Mount-, UTS-, IPC-, PID-, Network-, 
+      User-Namespace-Examination. Unshare-Command-Analysis, Process-Container-
+      Mapping, Resource-Limitation-Detection via /sys/fs/cgroup für 
+      Container-Escape-Investigation und Privilege-Escalation-Detection.
+    url: https://cloud.cc24.dev/f/32342
+    skillLevel: expert
+    domains:
+      - incident-response
+      - malware-analysis
+    phases:
+      - examination
+      - analysis
+    tags:
+      - container-analysis
+      - namespace-isolation
+      - privilege-escalation
+      - process-isolation
+      - container-escape
+      - cgroup-analysis
+    related_concepts:
+      - Memory Forensics & Process Analysis
+  - name: Docker Container Forensische Analyse
+    type: method
+    description: >-
+      Docker-Image/Container-Layer-Analysis für Containerized-Application-
+      Forensics. Dockerfile-Reconstruction, Image-History-Analysis, 
+      Container-Runtime-Investigation, Volume-Mount-Analysis und 
+      Network-Bridge-Examination für Container-Security-Incident-Response.
+    url: https://cloud.cc24.dev/f/32342
+    skillLevel: advanced
+    domains:
+      - incident-response
+      - cloud-forensics
+      - malware-analysis
+    phases:
+      - examination
+      - analysis
+    tags:
+      - container-forensics
+      - docker-analysis
+      - image-analysis
+      - layer-analysis
+      - volume-analysis
+      - containerized-malware
+    related_concepts:
+      - Memory Forensics & Process Analysis
+    related_software:
+      - Docker Explorer
+  - name: Linux System Monitoring Forensik
+    type: method
+    description: >-
+      Live-System-Überwachung durch ps, top, htop für Process-Analysis und 
+      Malware-Detection. SHA256-Hash-Verification von System-Binaries, 
+      Update-Status-Verification, Hidden-Process-Detection und 
+      Resource-Usage-Anomaly-Analysis für APT-Investigation.
+    url: https://cloud.cc24.dev/f/32342
+    skillLevel: intermediate
+    domains:
+      - incident-response
+      - malware-analysis
+    phases:
+      - examination
+      - analysis
+    tags:
+      - process-monitoring
+      - hash-verification
+      - malware-detection
+      - system-integrity
+      - anomaly-detection
+      - apt-investigation
+    related_concepts:
+      - Hash Functions & Digital Signatures
+      - Memory Forensics & Process Analysis
+    related_software:
+      - hashdeep
+      - md5sum / sha256sum
+  - name: DNS Filtering Analysis (Linux)
+    type: method
+    description: >-
+      /etc/hosts-File-Analysis für DNS-Manipulation-Detection und 
+      Domain-Blocking-Investigation. Static-DNS-Entry-Analysis, 
+      Malware-DNS-Hijacking-Detection, Sinkhole-Configuration-Analysis für 
+      Network-Traffic-Redirection-Investigation und Anti-Malware-Verification.
+    url: https://cloud.cc24.dev/f/32342
+    skillLevel: beginner
+    domains:
+      - incident-response
+      - network-forensics
+      - malware-analysis
+    phases:
+      - examination
+      - analysis
+    tags:
+      - dns-analysis
+      - hosts-file
+      - domain-blocking
+      - dns-hijacking
+      - traffic-redirection
+      - sinkhole-analysis
+    related_concepts:
+      - Network Protocols & Packet Analysis
+  - name: Linux Route Filtering Forensik
+    type: method
+    description: >-
+      Routing-Table-Analysis durch route-Command für Network-Traffic-Flow-
+      Investigation. Static-Route-Configuration-Analysis, Gateway-Manipulation-
+      Detection, Traffic-Filtering-Verification für Network-Isolation-Analysis 
+      und Lateral-Movement-Investigation.
+    url: https://cloud.cc24.dev/f/32342
+    skillLevel: intermediate
+    domains:
+      - incident-response
+      - network-forensics
+    phases:
+      - examination
+      - analysis
+    tags:
+      - routing-analysis
+      - traffic-filtering
+      - gateway-analysis
+      - network-isolation
+      - lateral-movement
+      - route-manipulation
+    related_concepts:
+      - Network Protocols & Packet Analysis
+  - name: GnuPG Verschlüsselungs-Forensik
+    type: method
+    description: >-
+      OpenPGP-Encryption-Analysis für verschlüsselte Kommunikation und 
+      File-Protection-Investigation. Public/Private-Key-Analysis, 
+      Signature-Verification, Encrypted-Message-Recovery-Techniques und 
+      Key-Ring-Analysis für Cryptographic-Evidence-Processing.
+    url: https://cloud.cc24.dev/f/32342
+    skillLevel: advanced
+    domains:
+      - static-investigations
+      - incident-response
+    phases:
+      - examination
+      - analysis
+    tags:
+      - encryption-analysis
+      - pgp-analysis
+      - key-analysis
+      - signature-verification
+      - encrypted-communications
+      - cryptographic-evidence
+    related_concepts:
+      - Hash Functions & Digital Signatures
+  - name: X.509 Certificate Analysis
+    type: method
+    description: >-
+      SSL/TLS-Certificate-Forensik für HTTPS-Communication-Analysis und 
+      PKI-Investigation. Certificate-Chain-Verification, CA-Trust-Analysis, 
+      Certificate-Metadata-Extraction, Expired/Revoked-Certificate-Detection 
+      für Network-Security-Incident-Analysis.
+    url: https://cloud.cc24.dev/f/32342
+    skillLevel: intermediate
+    domains:
+      - incident-response
+      - network-forensics
+    phases:
+      - examination
+      - analysis
+    tags:
+      - certificate-analysis
+      - ssl-tls-analysis
+      - pki-analysis
+      - trust-verification
+      - network-security
+      - https-analysis
+    related_concepts:
+      - Hash Functions & Digital Signatures
+      - Network Protocols & Packet Analysis
+  - name: USB Device Forensic Analysis (Linux)
+    type: method
+    description: >-
+      USB-Hardware-Forensik durch usbauth-Framework für Device-Access-Control-
+      Analysis. USB-Device-History-Reconstruction, Vendor/Product-ID-Analysis, 
+      Port-Binding-Investigation und Device-Type-Classification für 
+      Hardware-Based-Attack-Investigation und Data-Exfiltration-Analysis.
+    url: https://cloud.cc24.dev/f/32342
+    skillLevel: intermediate
+    domains:
+      - incident-response
+      - static-investigations
+    phases:
+      - examination
+      - analysis
+    tags:
+      - usb-forensics
+      - hardware-analysis
+      - device-history
+      - access-control
+      - data-exfiltration
+      - hardware-attacks
+    related_concepts:
+      - Timeline Analysis & Event Correlation
+  - name: Linux Live-Boot Forensik-Umgebung
+    type: method
+    description: >-
+      Kontaminationsfreie Systemuntersuchung durch Boot von USB/DVD ohne 
+      Festplatten-Zugriff. Forensics-Mode deaktiviert automatisches Mounting 
+      und Netzwerk-Services. Ermöglicht saubere Evidence-Akquisition ohne 
+      Host-System-Veränderungen. Alle Änderungen sind nach Ausschalten gelöscht.
+    url: https://cloud.cc24.dev/f/32342
+    skillLevel: beginner
+    domains:
+      - incident-response
+      - static-investigations
+    phases:
+      - data-collection
+      - examination
+    tags:
+      - live-acquisition
+      - write-blocker
+      - contamination-prevention
+      - forensic-imaging
+      - read-only-access
+    related_concepts:
+      - Digital Evidence Chain of Custody
+    related_software:
+      - Kali Linux
+      - SIFT Workstation
+      - CAINE
+  - name: Linux Festplatten-Partitionierung
+    type: method
+    description: >-
+      Systematische Datenträger-Aufteilung mit fdisk (MBR) oder gdisk (GPT)  für
+      forensische Imaging-Workflows. Unterstützt DOS-, GPT-, SGI- und 
+      Sun-Partitionierungen. Kombiniert mit mkfs für Dateisystem-Erstellung. 
+      Essentiell für Evidence-Storage und Analyse-Partitionen.
+    url: https://cloud.cc24.dev/f/32342
+    skillLevel: intermediate
+    domains:
+      - incident-response
+      - static-investigations
+    phases:
+      - data-collection
+      - examination
+    tags:
+      - disk-partitioning
+      - gpt-analysis
+      - mbr-analysis
+      - filesystem-creation
+      - evidence-storage
+    related_concepts:
+      - File Systems & Storage Forensics
+      - Digital Evidence Chain of Custody
+    related_software:
+      - dd
+      - FTK Imager
+  - name: Linux Benutzer- und Rechteverwaltung
+    type: method
+    description: >-
+      Systematische Analyse von /etc/passwd, /etc/shadow und /etc/group für 
+      User-Activity-Reconstruction. Zahlencodierung (chmod 755) und 
+      Symbolic-Notation (rwxr-xr-x) für Dateiberechtigungen. Includes setuid, 
+      setgid und sticky-bit Analyse für Privilege-Escalation-Detection.
+    url: https://cloud.cc24.dev/f/32342
+    skillLevel: intermediate
+    domains:
+      - incident-response
+      - static-investigations
+      - malware-analysis
+    phases:
+      - examination
+      - analysis
+    tags:
+      - user-analysis
+      - permission-analysis
+      - privilege-escalation
+      - system-metadata
+      - access-control
+    related_concepts:
+      - File Systems & Storage Forensics
+      - Timeline Analysis & Event Correlation
+    related_software:
+      - grep
+      - strings
+  - name: Linux Log-Analyse Methodik
+    type: method
+    description: >-
+      Strukturierte Auswertung von /var/log/* für
+      Incident-Timeline-Reconstruction.  Unix-Time-Konvertierung,
+      Kernel-Ring-Buffer-Analyse mit dmesg,  Authentication-Logs in
+      auth.log/secure. Korrelation zwischen System-,  Kernel- und
+      Application-Logs für vollständige Event-Sequencing.
+    url: https://cloud.cc24.dev/f/32342
+    skillLevel: intermediate
+    domains:
+      - incident-response
+      - static-investigations
+      - network-forensics
+    phases:
+      - examination
+      - analysis
+    tags:
+      - log-analysis
+      - timeline-analysis
+      - unix-time
+      - kernel-analysis
+      - authentication-logs
+      - syslog-analysis
+    related_concepts:
+      - Timeline Analysis & Event Correlation
+    related_software:
+      - grep
+      - Plaso (log2timeline)
+  - name: Linux Shell-Historie Forensik
+    type: method
+    description: >-
+      Bash-History-Analyse aus ~/.bash_history für Command-Execution-Timeline. 
+      Erkennung von Anti-Forensik-Techniken: geleerte History, Softlinks auf 
+      /dev/null, manipulierte Timestamps. Korrelation mit Login-Logs für 
+      vollständige User-Activity-Reconstruction ohne Zeitstempel.
+    url: https://cloud.cc24.dev/f/32342
+    skillLevel: intermediate
+    domains:
+      - incident-response
+      - static-investigations
+      - malware-analysis
+    phases:
+      - examination
+      - analysis
+    tags:
+      - command-history
+      - user-activity
+      - anti-forensics-detection
+      - bash-analysis
+      - timeline-reconstruction
+      - indicator-of-compromise
+    related_concepts:
+      - Timeline Analysis & Event Correlation
+    related_software:
+      - grep
+      - strings
+  - name: Git Repository Forensik
+    type: method
+    description: >-
+      Versionskontroll-System-Analyse für Source-Code-Forensik und 
+      Entwickler-Activity-Tracking. Branch-Timeline-Reconstruction, 
+      Commit-Hash-Verifizierung, Merge-Conflict-Analysis. Collaborative-
+      Workflow-Investigation über Remote-Repositories und Pull-Requests.
+    url: https://cloud.cc24.dev/f/32342
+    skillLevel: advanced
+    domains:
+      - incident-response
+      - static-investigations
+      - malware-analysis
+    phases:
+      - examination
+      - analysis
+    tags:
+      - version-control
+      - source-code-analysis
+      - developer-tracking
+      - commit-analysis
+      - collaboration-forensics
+      - hash-verification
+    related_concepts:
+      - Hash Functions & Digital Signatures
+      - Timeline Analysis & Event Correlation
+    related_software:
+      - Gitea
+  - name: iptables Firewall-Forensik
+    type: method
+    description: >-
+      Netzwerk-Traffic-Filtering-Analysis durch iptables-Rules-Reconstruction. 
+      INPUT/OUTPUT/FORWARD-Chain-Analysis, ACCEPT/DROP/REJECT-Actions, 
+      Port-based und IP-based Filtering. Regel-Persistence-Verification und 
+      Anti-Evasion-Configuration für Incident-Response.
+    url: https://cloud.cc24.dev/f/32342
+    skillLevel: advanced
+    domains:
+      - incident-response
+      - network-forensics
+    phases:
+      - examination
+      - analysis
+    tags:
+      - firewall-analysis
+      - network-filtering
+      - traffic-analysis
+      - rule-analysis
+      - packet-filtering
+      - network-security
+    related_concepts:
+      - Network Protocols & Packet Analysis
+  - name: LUKS Disk-Encryption Analysis
+    type: method
+    description: >-
+      Linux Unified Key Setup Verschlüsselungs-Forensik für encrypted 
+      Partitions und Container. Cryptsetup-based Key-Slot-Analysis, 
+      Metadata-Header-Examination, Passphrase-Recovery-Techniques.  Integration
+      mit Filesystem-Mounting für decrypted Evidence-Access.
+    url: https://cloud.cc24.dev/f/32342
+    skillLevel: expert
+    domains:
+      - static-investigations
+      - incident-response
+    phases:
+      - examination
+      - analysis
+    tags:
+      - disk-encryption
+      - key-analysis
+      - encrypted-storage
+      - partition-analysis
+      - cryptographic-analysis
+      - access-recovery
+    related_concepts:
+      - Hash Functions & Digital Signatures
+      - File Systems & Storage Forensics
+  - name: Linux Secure File Deletion Verification
+    type: method
+    description: >-
+      Forensische Verifikation sicherer Löschvorgänge durch Overwrite-Pattern-
+      Analysis. Unterscheidung zwischen rm (Filesystem-Entry-Deletion) und 
+      shred (Multi-Pass-Overwriting). Recovery-Verification und Anti-Forensik-
+      Detection bei unvollständigen Secure-Wipe-Operationen.
+    url: https://cloud.cc24.dev/f/32342
+    skillLevel: intermediate
+    domains:
+      - static-investigations
+      - incident-response
+    phases:
+      - examination
+      - analysis
+    tags:
+      - secure-deletion
+      - data-recovery
+      - overwrite-analysis
+      - anti-forensics
+      - file-system-analysis
+      - data-sanitization
+    related_concepts:
+      - File Systems & Storage Forensics
+    related_software:
+      - PhotoRec
+      - dd
+  - name: Linux Process Memory Analysis
+    type: method
+    description: >-
+      Live-System-Speicher-Untersuchung durch /proc-Filesystem-Analysis. 
+      Process-State-Examination, Memory-Maps-Analysis, Open-Files-Detection  via
+      /proc/PID/. Kernel-Module-Verification und Hidden-Process-Detection  für
+      Rootkit-Analysis ohne Memory-Dump-Tools.
+    url: https://cloud.cc24.dev/f/32342
+    skillLevel: advanced
+    domains:
+      - incident-response
+      - malware-analysis
+    phases:
+      - examination
+      - analysis
+    tags:
+      - process-analysis
+      - memory-analysis
+      - proc-filesystem
+      - rootkit-detection
+      - live-analysis
+      - kernel-analysis
+    related_concepts:
+      - Memory Forensics & Process Analysis
 domains:
   - id: incident-response
     name: Incident Response & Breach-Untersuchung
diff --git a/src/env.d.ts b/src/env.d.ts
index 26358c4..f8019ef 100644
--- a/src/env.d.ts
+++ b/src/env.d.ts
@@ -11,6 +11,9 @@ declare global {
     showToolDetails: (toolName: string, modalType?: string) => void;
     hideToolDetails: (modalType?: string) => void;
     hideAllToolDetails: () => void;
+    matrixShowToolDetails?: (toolName: string, modalType?: string) => void;
+    matrixHideToolDetails?: (modalType?: string) => void;
+    
     toggleKbEntry: (entryId: string) => void;
     toggleDomainAgnosticSection: (sectionId: string) => void;
     restoreAIResults?: () => void;
@@ -39,6 +42,9 @@ declare global {
     toggleAllScenarios?: () => void;
     showShareDialog?: (shareButton: Element) => void;
     modalHideInProgress?: boolean;
+    
+    shareArticle: (button: HTMLElement, url: string, title: string) => Promise<void>;
+    shareCurrentArticle: (button: HTMLElement) => Promise<void>;
   }
 }
 
diff --git a/src/layouts/BaseLayout.astro b/src/layouts/BaseLayout.astro
index 9462ec8..6330d69 100644
--- a/src/layouts/BaseLayout.astro
+++ b/src/layouts/BaseLayout.astro
@@ -2,6 +2,10 @@
 import Navigation from '../components/Navigation.astro';
 import Footer from '../components/Footer.astro';
 import '../styles/global.css';
+import '../styles/auditTrail.css';
+import '../styles/knowledgebase.css';
+import '../styles/palette.css';
+import '../styles/autocomplete.css';
 
 export interface Props {
   title: string;
@@ -21,36 +25,44 @@ const { title, description = 'ForensicPathways - A comprehensive directory of di
   <link rel="icon" type="image/x-icon" href="/favicon.ico">
   
 <script>
-  function createToolSlug(toolName) {
-    if (!toolName || typeof toolName !== 'string') {
-      console.warn('[toolHelpers] Invalid toolName provided to createToolSlug:', toolName);
-      return '';
+  async function loadUtilityFunctions() {
+      try {
+        const { createToolSlug, findToolByIdentifier, isToolHosted } = await import('../utils/clientUtils.js');
+        
+        (window as any).createToolSlug = createToolSlug;
+        (window as any).findToolByIdentifier = findToolByIdentifier;
+        (window as any).isToolHosted = isToolHosted;
+        
+        console.log('[UTILS] Utility functions loaded successfully');
+      } catch (error) {
+        console.error('Failed to load utility functions:', error);
+        
+        // Provide fallback implementations
+        (window as any).createToolSlug = (toolName: string) => {
+          if (!toolName || typeof toolName !== 'string') return '';
+          return toolName.toLowerCase().replace(/[^a-z0-9\s-]/g, '').replace(/\s+/g, '-').replace(/-+/g, '-').replace(/^-|-$/g, '');
+        };
+        
+        (window as any).findToolByIdentifier = (tools: any[], identifier: string) => {
+          if (!identifier || !Array.isArray(tools)) return undefined;
+          return tools.find((tool: any) => 
+            tool.name === identifier || 
+            (window as any).createToolSlug(tool.name) === identifier.toLowerCase()
+          );
+        };
+        
+        (window as any).isToolHosted = (tool: any) => {
+          return tool.projectUrl !== undefined && 
+                tool.projectUrl !== null && 
+                tool.projectUrl !== "" && 
+                tool.projectUrl.trim() !== "";
+        };
+        
+        console.log('[UTILS] Fallback utility functions registered');
+      }
     }
-    
-    return toolName.toLowerCase()
-      .replace(/[^a-z0-9\s-]/g, '')     // Remove special characters
-      .replace(/\s+/g, '-')             // Replace spaces with hyphens
-      .replace(/-+/g, '-')              // Remove duplicate hyphens
-      .replace(/^-|-$/g, '');           // Remove leading/trailing hyphens
-  }
 
-  function findToolByIdentifier(tools, identifier) {
-    if (!identifier || !Array.isArray(tools)) return undefined;
-    
-    return tools.find(tool => 
-      tool.name === identifier || 
-      createToolSlug(tool.name) === identifier.toLowerCase()
-    );
-  }
-
-  function isToolHosted(tool) {
-    return tool.projectUrl !== undefined && 
-           tool.projectUrl !== null && 
-           tool.projectUrl !== "" && 
-           tool.projectUrl.trim() !== "";
-  }
-
-  function scrollToElement(element, options = {}) {
+  function scrollToElement(element: Element | null, options = {}) {
     if (!element) return;
     
     setTimeout(() => {
@@ -66,17 +78,21 @@ const { title, description = 'ForensicPathways - A comprehensive directory of di
     }, 100);
   }
 
-  function scrollToElementById(elementId, options = {}) {
+  function scrollToElementById(elementId: string, options = {}) {
     const element = document.getElementById(elementId);
-    scrollToElement(element, options);
+    if (element) {
+      scrollToElement(element, options);
+    }
   }
 
-  function scrollToElementBySelector(selector, options = {}) {
+  function scrollToElementBySelector(selector: string, options = {}) {
     const element = document.querySelector(selector);
-    scrollToElement(element, options);
+    if (element) {
+      scrollToElement(element, options);
+    }
   }
 
-  function prioritizeSearchResults(tools, searchTerm) {
+  function prioritizeSearchResults(tools: any[], searchTerm: string) {
     if (!searchTerm || !searchTerm.trim()) {
       return tools;
     }
@@ -84,8 +100,8 @@ const { title, description = 'ForensicPathways - A comprehensive directory of di
     const lowerSearchTerm = searchTerm.toLowerCase().trim();
     
     return tools.sort((a, b) => {
-      const aTagsLower = (a.tags || []).map(tag => tag.toLowerCase());
-      const bTagsLower = (b.tags || []).map(tag => tag.toLowerCase());
+      const aTagsLower = (a.tags || []).map((tag: string) => tag.toLowerCase());
+      const bTagsLower = (b.tags || []).map((tag: string) => tag.toLowerCase());
       
       const aExactTag = aTagsLower.includes(lowerSearchTerm);
       const bExactTag = bTagsLower.includes(lowerSearchTerm);
@@ -97,15 +113,15 @@ const { title, description = 'ForensicPathways - A comprehensive directory of di
     });
   }
 
-  (window as any).createToolSlug = createToolSlug;
-  (window as any).findToolByIdentifier = findToolByIdentifier;
-  (window as any).isToolHosted = isToolHosted;
   (window as any).scrollToElement = scrollToElement;
   (window as any).scrollToElementById = scrollToElementById;
   (window as any).scrollToElementBySelector = scrollToElementBySelector;
   (window as any).prioritizeSearchResults = prioritizeSearchResults;
 
-  document.addEventListener('DOMContentLoaded', () => {
+  document.addEventListener('DOMContentLoaded', async () => {
+    // CRITICAL: Load utility functions FIRST before any URL handling
+    await loadUtilityFunctions();
+    
     const THEME_KEY = 'dfir-theme';
 
     function getSystemTheme() {
@@ -116,12 +132,12 @@ const { title, description = 'ForensicPathways - A comprehensive directory of di
       return localStorage.getItem(THEME_KEY) || 'auto';
     }
 
-    function applyTheme(theme) {
+    function applyTheme(theme: string) {
       const effectiveTheme = theme === 'auto' ? getSystemTheme() : theme;
       document.documentElement.setAttribute('data-theme', effectiveTheme);
     }
 
-    function updateThemeToggle(theme) {
+    function updateThemeToggle(theme: string) {
       document.querySelectorAll('[data-theme-toggle]').forEach(button => {
         button.setAttribute('data-current-theme', theme);
       });
@@ -156,6 +172,44 @@ const { title, description = 'ForensicPathways - A comprehensive directory of di
       toggleTheme,
       getStoredTheme
     };
+  
+  (window as any).showToolDetails = function(toolName: string, modalType: string = 'primary') {
+    
+    let attempts = 0;
+    const maxAttempts = 50;
+    
+    const tryDelegate = () => {
+      const matrixShowToolDetails = (window as any).matrixShowToolDetails;
+      
+      if (matrixShowToolDetails && typeof matrixShowToolDetails === 'function') {
+        return matrixShowToolDetails(toolName, modalType);
+      }
+      
+      const directShowToolDetails = (window as any).directShowToolDetails;
+      if (directShowToolDetails && typeof directShowToolDetails === 'function') {
+        return directShowToolDetails(toolName, modalType);
+      }
+      
+      attempts++;
+      if (attempts < maxAttempts) {
+        setTimeout(tryDelegate, 100);
+      } else {
+      }
+    };
+    
+    tryDelegate();
+  };
+
+    (window as any).hideToolDetails = function(modalType: string = 'both') {
+      const matrixHideToolDetails = (window as any).matrixHideToolDetails;
+      if (matrixHideToolDetails && typeof matrixHideToolDetails === 'function') {
+        return matrixHideToolDetails(modalType);
+      }
+    };
+
+    (window as any).hideAllToolDetails = function() {
+      (window as any).hideToolDetails('both');
+    };
 
     async function checkClientAuth(context = 'general') {
       try {
@@ -191,7 +245,7 @@ const { title, description = 'ForensicPathways - A comprehensive directory of di
       }
     }
 
-    async function requireClientAuth(callback, returnUrl, context = 'general') {
+    async function requireClientAuth(callback: () => void, returnUrl: string, context = 'general') {
       const authStatus = await checkClientAuth(context);
       
       if (authStatus.authRequired && !authStatus.authenticated) {
@@ -206,12 +260,12 @@ const { title, description = 'ForensicPathways - A comprehensive directory of di
       }
     }
 
-    async function showIfAuthenticated(selector, context = 'general') {
+    async function showIfAuthenticated(selector: string, context = 'general') {
       const authStatus = await checkClientAuth(context);
       const element = document.querySelector(selector);
       
       if (element) {
-        element.style.display = (!authStatus.authRequired || authStatus.authenticated) 
+        (element as HTMLElement).style.display = (!authStatus.authRequired || authStatus.authenticated) 
           ? 'inline-flex' 
           : 'none';
       }
@@ -240,6 +294,51 @@ const { title, description = 'ForensicPathways - A comprehensive directory of di
     (window as any).showIfAuthenticated = showIfAuthenticated;
     (window as any).setupAuthButtons = setupAuthButtons;
 
+    async function copyUrlToClipboard(url: string, button: HTMLElement) {
+      try {
+        await navigator.clipboard.writeText(url);
+        
+        const originalHTML = button.innerHTML;
+        button.innerHTML = `
+          <svg width="14" height="14" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" style="margin-right: 0.375rem;">
+            <polyline points="20,6 9,17 4,12"/>
+          </svg>
+          Kopiert!
+        `;
+        button.style.color = 'var(--color-accent)';
+        
+        setTimeout(() => {
+          button.innerHTML = originalHTML;
+          button.style.color = '';
+        }, 2000);
+      } catch (err) {
+        const textArea = document.createElement('textarea');
+        textArea.value = url;
+        document.body.appendChild(textArea);
+        textArea.select();
+        document.execCommand('copy');
+        document.body.removeChild(textArea);
+        
+        const originalHTML = button.innerHTML;
+        button.innerHTML = 'Kopiert!';
+        setTimeout(() => {
+          button.innerHTML = originalHTML;
+        }, 2000);
+      }
+    }
+
+    async function shareArticle(button: HTMLElement, url: string, title: string) {
+      const fullUrl = window.location.origin + url;
+      await copyUrlToClipboard(fullUrl, button);
+    }
+
+    async function shareCurrentArticle(button: HTMLElement) {
+      await copyUrlToClipboard(window.location.href, button);
+    }
+
+    (window as any).shareArticle = shareArticle;
+    (window as any).shareCurrentArticle = shareCurrentArticle;
+
     initTheme();
     setupAuthButtons('[data-contribute-button]');
     
@@ -247,8 +346,6 @@ const { title, description = 'ForensicPathways - A comprehensive directory of di
       await showIfAuthenticated('#ai-view-toggle', 'ai');
     };
     initAIButton();
-    
-    console.log('[CONSOLIDATED] All utilities loaded and initialized');
   });
 </script>
 </head>
diff --git a/src/pages/api/ai/embeddings-status.ts b/src/pages/api/ai/embeddings-status.ts
new file mode 100644
index 0000000..9d9b9c8
--- /dev/null
+++ b/src/pages/api/ai/embeddings-status.ts
@@ -0,0 +1,37 @@
+// src/pages/api/ai/embeddings-status.ts
+import type { APIRoute } from 'astro';
+
+export const prerender = false;
+
+export const GET: APIRoute = async () => {
+  try {
+    const { embeddingsService } = await import('../../../utils/embeddings.js');
+    await embeddingsService.waitForInitialization();
+    
+    const stats = embeddingsService.getStats();
+    const status = stats.enabled && stats.initialized ? 'ready' : 
+                  stats.enabled && !stats.initialized ? 'initializing' : 'disabled';
+    
+    return new Response(JSON.stringify({
+      success: true,
+      embeddings: stats,
+      timestamp: new Date().toISOString(),
+      status: status
+    }), {
+      status: 200,
+      headers: { 'Content-Type': 'application/json' }
+    });
+    
+  } catch (error) {
+    return new Response(JSON.stringify({
+      success: false,
+      embeddings: { enabled: false, initialized: false, count: 0 },
+      timestamp: new Date().toISOString(),
+      status: 'disabled',
+      error: error.message
+    }), {
+      status: 200,
+      headers: { 'Content-Type': 'application/json' }
+    });
+  }
+};
\ No newline at end of file
diff --git a/src/pages/api/ai/embeddings.status.ts b/src/pages/api/ai/embeddings.status.ts
deleted file mode 100644
index 2ca02c2..0000000
--- a/src/pages/api/ai/embeddings.status.ts
+++ /dev/null
@@ -1,22 +0,0 @@
-// src/pages/api/ai/embeddings-status.ts
-import type { APIRoute } from 'astro';
-import { embeddingsService } from '../../../utils/embeddings.js';
-import { apiResponse, apiServerError } from '../../../utils/api.js';
-
-export const prerender = false;
-
-export const GET: APIRoute = async () => {
-  try {
-    const stats = embeddingsService.getStats();
-    
-    return apiResponse.success({
-      embeddings: stats,
-      timestamp: new Date().toISOString(),
-      status: stats.enabled && stats.initialized ? 'ready' : 
-             stats.enabled && !stats.initialized ? 'initializing' : 'disabled'
-    });
-  } catch (error) {
-    console.error('Embeddings status error:', error);
-    return apiServerError.internal('Failed to get embeddings status');
-  }
-};
\ No newline at end of file
diff --git a/src/pages/api/ai/enhance-input.ts b/src/pages/api/ai/enhance-input.ts
index 1ebb2a8..392ca86 100644
--- a/src/pages/api/ai/enhance-input.ts
+++ b/src/pages/api/ai/enhance-input.ts
@@ -20,7 +20,7 @@ const AI_ANALYZER_API_KEY = getEnv('AI_ANALYZER_API_KEY');
 const AI_ANALYZER_MODEL = getEnv('AI_ANALYZER_MODEL');
 
 const rateLimitStore = new Map<string, { count: number; resetTime: number }>();
-const RATE_LIMIT_WINDOW = 60 * 1000; // 1 minute
+const RATE_LIMIT_WINDOW = 60 * 1000;
 const RATE_LIMIT_MAX = 5;
 
 function sanitizeInput(input: string): string {
@@ -81,12 +81,13 @@ QUALITÄTSKRITERIEN FÜR FRAGEN:
 - Forensisch spezifisch, nicht allgemein (❌ "Mehr Details?" ✅ "Welche forensischen Artefakte (RAM-Dumps, Disk-Images, Logs) stehen zur Verfügung?")
 - Methodisch relevant (❌ "Wann passierte das?" ✅ "Liegen Log-Dateien aus dem Incident-Zeitraum vor, und welche Retention-Policy gilt?")
 - Priorisiert nach Auswirkung auf die forensische Untersuchungsqualität
+- Die Frage soll maximal 30 Wörter umfassen
 
 ANTWORTFORMAT (NUR JSON, KEIN ZUSÄTZLICHER TEXT):
 [
-  "Forensisch spezifische Frage 1?",
-  "Forensisch spezifische Frage 2?",
-  "Forensisch spezifische Frage 3?"
+  "spezifische Frage 1?",
+  "spezifische Frage 2?",
+  "spezifische Frage 3?"
 ]
 
 NUTZER-EINGABE:
diff --git a/src/pages/api/ai/query.ts b/src/pages/api/ai/query.ts
index 19eefd8..2e94071 100644
--- a/src/pages/api/ai/query.ts
+++ b/src/pages/api/ai/query.ts
@@ -1,4 +1,4 @@
-// src/pages/api/ai/query.ts - FIXED: Rate limiting for micro-task pipeline
+// src/pages/api/ai/query.ts
 
 import type { APIRoute } from 'astro';
 import { withAPIAuth } from '../../../utils/auth.js';
diff --git a/src/pages/api/contribute/tool.ts b/src/pages/api/contribute/tool.ts
index 7d656f1..1cb90d8 100644
--- a/src/pages/api/contribute/tool.ts
+++ b/src/pages/api/contribute/tool.ts
@@ -1,4 +1,4 @@
-// src/pages/api/contribute/tool.ts (UPDATED - Using consolidated API responses)
+// src/pages/api/contribute/tool.ts (UPDATED - Using consolidated API responses + related_software)
 import type { APIRoute } from 'astro';
 import { withAPIAuth } from '../../../utils/auth.js';
 import { apiResponse, apiError, apiServerError, apiSpecial, handleAPIRequest } from '../../../utils/api.js';
@@ -27,6 +27,7 @@ const ContributionToolSchema = z.object({
   knowledgebase: z.boolean().optional().nullable(),
   'domain-agnostic-software': z.array(z.string()).optional().nullable(),
   related_concepts: z.array(z.string()).optional().nullable(),
+  related_software: z.array(z.string()).optional().nullable(),
   tags: z.array(z.string()).default([]),
   statusUrl: z.string().url('Must be a valid URL').optional().nullable()
 });
@@ -80,6 +81,38 @@ function sanitizeInput(obj: any): any {
   return obj;
 }
 
+function preprocessFormData(body: any): any {
+  // Handle comma-separated strings from autocomplete inputs
+  if (body.tool) {
+    // Handle tags
+    if (typeof body.tool.tags === 'string') {
+      body.tool.tags = body.tool.tags.split(',').map((t: string) => t.trim()).filter(Boolean);
+    }
+    
+    // Handle related concepts
+    if (body.tool.relatedConcepts) {
+      if (typeof body.tool.relatedConcepts === 'string') {
+        body.tool.related_concepts = body.tool.relatedConcepts.split(',').map((t: string) => t.trim()).filter(Boolean);
+      } else {
+        body.tool.related_concepts = body.tool.relatedConcepts;
+      }
+      delete body.tool.relatedConcepts; // Remove the original key
+    }
+    
+    // Handle related software
+    if (body.tool.relatedSoftware) {
+      if (typeof body.tool.relatedSoftware === 'string') {
+        body.tool.related_software = body.tool.relatedSoftware.split(',').map((t: string) => t.trim()).filter(Boolean);
+      } else {
+        body.tool.related_software = body.tool.relatedSoftware;
+      }
+      delete body.tool.relatedSoftware; // Remove the original key
+    }
+  }
+  
+  return body;
+}
+
 async function validateToolData(tool: any, action: string): Promise<{ valid: boolean; errors: string[] }> {
   const errors: string[] = [];
   
@@ -109,6 +142,17 @@ async function validateToolData(tool: any, action: string): Promise<{ valid: boo
       }
     }
     
+    // Validate related items exist (optional validation - could be enhanced)
+    if (tool.related_concepts && tool.related_concepts.length > 0) {
+      // Could validate that referenced concepts actually exist
+      console.log('[VALIDATION] Related concepts provided:', tool.related_concepts);
+    }
+    
+    if (tool.related_software && tool.related_software.length > 0) {
+      // Could validate that referenced software actually exists
+      console.log('[VALIDATION] Related software provided:', tool.related_software);
+    }
+    
     return { valid: errors.length === 0, errors };
     
   } catch (error) {
@@ -143,6 +187,9 @@ export const POST: APIRoute = async ({ request }) => {
       return apiSpecial.invalidJSON();
     }
 
+    // Preprocess form data to handle autocomplete inputs
+    body = preprocessFormData(body);
+    
     const sanitizedBody = sanitizeInput(body);
 
     let validatedData;
@@ -153,6 +200,7 @@ export const POST: APIRoute = async ({ request }) => {
         const errorMessages = error.errors.map(err => 
           `${err.path.join('.')}: ${err.message}`
         );
+        console.log('[VALIDATION] Zod validation errors:', errorMessages);
         return apiError.validation('Validation failed', errorMessages);
       }
       
@@ -174,6 +222,16 @@ export const POST: APIRoute = async ({ request }) => {
       }
     };
 
+    console.log('[CONTRIBUTION] Processing contribution:', {
+      type: contributionData.type,
+      toolName: contributionData.tool.name,
+      toolType: contributionData.tool.type,
+      submitter: userEmail,
+      hasRelatedConcepts: !!(contributionData.tool.related_concepts?.length),
+      hasRelatedSoftware: !!(contributionData.tool.related_software?.length),
+      tagsCount: contributionData.tool.tags?.length || 0
+    });
+
     try {
       const gitManager = new GitContributionManager();
       const result = await gitManager.submitContribution(contributionData);
diff --git a/src/pages/api/search/semantic.ts b/src/pages/api/search/semantic.ts
new file mode 100644
index 0000000..1a2b262
--- /dev/null
+++ b/src/pages/api/search/semantic.ts
@@ -0,0 +1,83 @@
+// src/pages/api/search/semantic.ts
+import type { APIRoute } from 'astro';
+import { getToolsData } from '../../../utils/dataService.js';
+import { configDotenv } from 'dotenv';
+
+configDotenv();    
+
+const DEFAULT_MAX_RESULTS = (() => {
+  const raw = process.env.AI_EMBEDDING_CANDIDATES;
+  const n   = Number.parseInt(raw ?? '', 10);
+  return Number.isFinite(n) && n > 0 ? n : 50;
+})();
+
+const DEFAULT_THRESHOLD = (() => {
+  const raw = process.env.AI_SIMILARITY_THRESHOLD;
+  const n   = Number.parseFloat(raw ?? '');
+  return Number.isFinite(n) && n >= 0 && n <= 1 ? n : 0.45;
+})();
+
+export const prerender = false;
+
+
+export const POST: APIRoute = async ({ request }) => {
+  try {
+    const {
+      query,
+      maxResults = DEFAULT_MAX_RESULTS,
+      threshold  = DEFAULT_THRESHOLD
+    } = await request.json();
+
+    if (!query || typeof query !== 'string') {
+      return new Response(
+        JSON.stringify({ success: false, error: 'Query is required' }),
+        { status: 400, headers: { 'Content-Type': 'application/json' } }
+      );
+    }
+
+    /* --- (rest of the handler unchanged) -------------------------- */
+    const { embeddingsService } = await import('../../../utils/embeddings.js');
+
+    if (!embeddingsService.isEnabled()) {
+      return new Response(
+        JSON.stringify({ success: false, error: 'Semantic search not available' }),
+        { status: 400, headers: { 'Content-Type': 'application/json' } }
+      );
+    }
+
+    await embeddingsService.waitForInitialization();
+
+    const similarItems = await embeddingsService.findSimilar(
+      query.trim(),
+      maxResults,
+      threshold
+    );
+
+    const toolsData  = await getToolsData();
+    const rankedTools = similarItems
+      .map((s, i) => {
+        const tool = toolsData.tools.find(t => t.name === s.name);
+        return tool ? { ...tool, _semanticSimilarity: s.similarity, _semanticRank: i + 1 } : null;
+      })
+      .filter(Boolean);
+
+    return new Response(
+      JSON.stringify({
+        success: true,
+        query: query.trim(),
+        results: rankedTools,
+        totalFound: rankedTools.length,
+        semanticSearch: true,
+        threshold,
+        maxSimilarity: rankedTools[0]?._semanticSimilarity ?? 0
+      }),
+      { status: 200, headers: { 'Content-Type': 'application/json' } }
+    );
+  } catch (error) {
+    console.error('Semantic search error:', error);
+    return new Response(
+      JSON.stringify({ success: false, error: 'Semantic search failed' }),
+      { status: 500, headers: { 'Content-Type': 'application/json' } }
+    );
+  }
+};
\ No newline at end of file
diff --git a/src/pages/api/upload/media.ts b/src/pages/api/upload/media.ts
index 536d019..c337eae 100644
--- a/src/pages/api/upload/media.ts
+++ b/src/pages/api/upload/media.ts
@@ -1,4 +1,3 @@
-// src/pages/api/upload/media.ts 
 import type { APIRoute } from 'astro';
 import { withAPIAuth } from '../../../utils/auth.js';
 import { apiResponse, apiError, apiServerError, apiSpecial, handleAPIRequest } from '../../../utils/api.js';
@@ -22,7 +21,9 @@ const UPLOAD_CONFIG = {
   maxFileSize: 50 * 1024 * 1024, // 50MB
   allowedTypes: new Set([
     'image/jpeg', 'image/jpg', 'image/png', 'image/gif', 'image/webp', 'image/svg+xml',
+    
     'video/mp4', 'video/webm', 'video/ogg', 'video/avi', 'video/mov',
+    
     'application/pdf',
     'application/msword',
     'application/vnd.openxmlformats-officedocument.wordprocessingml.document',
@@ -30,7 +31,29 @@ const UPLOAD_CONFIG = {
     'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet',
     'application/vnd.ms-powerpoint',
     'application/vnd.openxmlformats-officedocument.presentationml.presentation',
-    'text/plain', 'text/csv', 'application/json'
+    
+    'text/plain', 
+    'text/csv', 
+    'text/markdown',          
+    'text/x-markdown',         
+    'application/json',
+    'application/xml',
+    'text/xml',
+    'text/html',
+    
+    'application/zip', 
+    'application/x-tar', 
+    'application/gzip',
+    'application/x-gzip',
+    'application/x-zip-compressed',
+    'application/x-rar-compressed',
+    'application/x-7z-compressed',
+    
+    'application/rtf',
+    'text/richtext',
+    'application/x-yaml',
+    'text/yaml',
+    'application/yaml'
   ]),
   localUploadPath: process.env.LOCAL_UPLOAD_PATH || './public/uploads',
   publicBaseUrl: process.env.PUBLIC_BASE_URL || 'http://localhost:4321'
@@ -50,6 +73,7 @@ function checkUploadRateLimit(userEmail: string): boolean {
   }
   
   if (userLimit.count >= RATE_LIMIT_MAX) {
+    console.warn(`[UPLOAD] Rate limit exceeded for user: ${userEmail} (${userLimit.count}/${RATE_LIMIT_MAX})`);
     return false;
   }
   
@@ -58,27 +82,37 @@ function checkUploadRateLimit(userEmail: string): boolean {
 }
 
 function validateFile(file: File): { valid: boolean; error?: string } {
+  console.log(`[UPLOAD] Validating file: ${file.name}, size: ${file.size}, type: ${file.type}`);
+  
   if (file.size > UPLOAD_CONFIG.maxFileSize) {
-    return { 
-      valid: false, 
-      error: `File too large. Maximum size is ${Math.round(UPLOAD_CONFIG.maxFileSize / 1024 / 1024)}MB` 
-    };
+    const errorMsg = `File too large. Maximum size is ${Math.round(UPLOAD_CONFIG.maxFileSize / 1024 / 1024)}MB`;
+    console.warn(`[UPLOAD] ${errorMsg} - File size: ${file.size}`);
+    return { valid: false, error: errorMsg };
   }
   
   if (!UPLOAD_CONFIG.allowedTypes.has(file.type)) {
-    return { 
-      valid: false, 
-      error: `File type ${file.type} not allowed` 
-    };
+    const errorMsg = `File type ${file.type} not allowed`;
+    console.warn(`[UPLOAD] ${errorMsg} - Allowed types:`, Array.from(UPLOAD_CONFIG.allowedTypes));
+    return { valid: false, error: errorMsg };
   }
   
+  console.log(`[UPLOAD] File validation passed for: ${file.name}`);
   return { valid: true };
 }
 
 async function uploadToNextcloud(file: File, userEmail: string): Promise<UploadResult> {
+  console.log(`[UPLOAD] Attempting Nextcloud upload for: ${file.name} by ${userEmail}`);
+  
   try {
     const uploader = new NextcloudUploader();
     const result = await uploader.uploadFile(file, userEmail);
+    
+    console.log(`[UPLOAD] Nextcloud upload successful:`, {
+      filename: result.filename,
+      url: result.url,
+      size: file.size
+    });
+    
     return {
       success: true,
       url: result.url,
@@ -87,7 +121,7 @@ async function uploadToNextcloud(file: File, userEmail: string): Promise<UploadR
       storage: 'nextcloud'
     };
   } catch (error) {
-    console.error('Nextcloud upload failed:', error);
+    console.error('[UPLOAD] Nextcloud upload failed:', error);
     return {
       success: false,
       error: error instanceof Error ? error.message : 'Nextcloud upload failed',
@@ -97,7 +131,10 @@ async function uploadToNextcloud(file: File, userEmail: string): Promise<UploadR
 }
 
 async function uploadToLocal(file: File, userType: string): Promise<UploadResult> {
+  console.log(`[UPLOAD] Attempting local upload for: ${file.name} (${userType})`);
+  
   try {
+    console.log(`[UPLOAD] Creating directory: ${UPLOAD_CONFIG.localUploadPath}`);
     await fs.mkdir(UPLOAD_CONFIG.localUploadPath, { recursive: true });
     
     const timestamp = new Date().toISOString().replace(/[:.]/g, '-');
@@ -106,11 +143,20 @@ async function uploadToLocal(file: File, userType: string): Promise<UploadResult
     const filename = `${timestamp}-${randomString}${extension}`;
     
     const filepath = path.join(UPLOAD_CONFIG.localUploadPath, filename);
+    console.log(`[UPLOAD] Writing file to: ${filepath}`);
+    
     const buffer = Buffer.from(await file.arrayBuffer());
     await fs.writeFile(filepath, buffer);
     
     const publicUrl = `${UPLOAD_CONFIG.publicBaseUrl}/uploads/${filename}`;
     
+    console.log(`[UPLOAD] Local upload successful:`, {
+      filename,
+      filepath,
+      publicUrl,
+      size: file.size
+    });
+    
     return {
       success: true,
       url: publicUrl,
@@ -119,7 +165,7 @@ async function uploadToLocal(file: File, userType: string): Promise<UploadResult
       storage: 'local'
     };
   } catch (error) {
-    console.error('Local upload failed:', error);
+    console.error('[UPLOAD] Local upload failed:', error);
     return {
       success: false,
       error: error instanceof Error ? error.message : 'Local upload failed',
@@ -130,12 +176,22 @@ async function uploadToLocal(file: File, userType: string): Promise<UploadResult
 
 export const POST: APIRoute = async ({ request }) => {
   return await handleAPIRequest(async () => {
+    console.log('[UPLOAD] Processing upload request');
+    
     const authResult = await withAPIAuth(request, 'contributions');
+    console.log('[UPLOAD] Auth result:', {
+      authenticated: authResult.authenticated,
+      authRequired: authResult.authRequired,
+      userId: authResult.userId
+    });
+    
     if (authResult.authRequired && !authResult.authenticated) {
-      return apiError.unauthorized();
+      console.warn('[UPLOAD] Upload rejected - authentication required but user not authenticated');
+      return apiError.unauthorized('Authentication required for file uploads');
     }
 
     const userEmail = authResult.session?.email || 'anon@anon.anon';
+    console.log(`[UPLOAD] Processing upload for user: ${userEmail}`);
 
     if (!checkUploadRateLimit(userEmail)) {
       return apiError.rateLimit('Upload rate limit exceeded. Please wait before uploading again.');
@@ -143,38 +199,59 @@ export const POST: APIRoute = async ({ request }) => {
 
     let formData;
     try {
+      console.log('[UPLOAD] Parsing form data');
       formData = await request.formData();
+      console.log('[UPLOAD] Form data keys:', Array.from(formData.keys()));
     } catch (error) {
-      return apiError.badRequest('Invalid form data');
+      console.error('[UPLOAD] Failed to parse form data:', error);
+      return apiError.badRequest('Invalid form data - could not parse request');
     }
 
     const file = formData.get('file') as File;
     const type = formData.get('type') as string;
 
     if (!file) {
+      console.warn('[UPLOAD] No file provided in request');
       return apiSpecial.missingRequired(['file']);
     }
 
+    console.log(`[UPLOAD] Processing file: ${file.name}, type parameter: ${type}`);
+
     const validation = validateFile(file);
     if (!validation.valid) {
       return apiError.badRequest(validation.error!);
     }
 
+    const nextcloudConfigured = isNextcloudConfigured();
+    console.log('[UPLOAD] Environment check:', {
+      nextcloudConfigured,
+      localUploadPath: UPLOAD_CONFIG.localUploadPath,
+      publicBaseUrl: UPLOAD_CONFIG.publicBaseUrl,
+      nodeEnv: process.env.NODE_ENV
+    });
+
     let result: UploadResult;
     
-    if (isNextcloudConfigured()) {
+    if (nextcloudConfigured) {
+      console.log('[UPLOAD] Using Nextcloud as primary storage');
       result = await uploadToNextcloud(file, userEmail);
       
       if (!result.success) {
-        console.warn('Nextcloud upload failed, trying local fallback:', result.error);
+        console.warn('[UPLOAD] Nextcloud upload failed, trying local fallback:', result.error);
         result = await uploadToLocal(file, type);
       }
     } else {
+      console.log('[UPLOAD] Using local storage (Nextcloud not configured)');
       result = await uploadToLocal(file, type);
     }
     
     if (result.success) {
-      console.log(`[MEDIA UPLOAD] ${file.name} (${file.size} bytes) by ${userEmail} -> ${result.storage}: ${result.url}`);
+      console.log(`[UPLOAD] Upload completed successfully:`, {
+        filename: result.filename,
+        storage: result.storage,
+        url: result.url,
+        user: userEmail
+      });
       
       return apiSpecial.uploadSuccess({
         url: result.url!,
@@ -183,7 +260,12 @@ export const POST: APIRoute = async ({ request }) => {
         storage: result.storage!
       });
     } else {
-      console.error(`[MEDIA UPLOAD FAILED] ${file.name} by ${userEmail}: ${result.error}`);
+      console.error(`[UPLOAD] Upload failed completely:`, {
+        filename: file.name,
+        error: result.error,
+        storage: result.storage,
+        user: userEmail
+      });
       
       return apiSpecial.uploadFailed(result.error!);
     }
@@ -193,6 +275,8 @@ export const POST: APIRoute = async ({ request }) => {
 
 export const GET: APIRoute = async ({ request }) => {
   return await handleAPIRequest(async () => {
+    console.log('[UPLOAD] Getting upload status');
+    
     const authResult = await withAPIAuth(request);
     if (authResult.authRequired && !authResult.authenticated) {
       return apiError.unauthorized();
@@ -204,12 +288,14 @@ export const GET: APIRoute = async ({ request }) => {
     try {
       await fs.access(UPLOAD_CONFIG.localUploadPath);
       localStorageAvailable = true;
+      console.log('[UPLOAD] Local storage accessible');
     } catch {
       try {
         await fs.mkdir(UPLOAD_CONFIG.localUploadPath, { recursive: true });
         localStorageAvailable = true;
+        console.log('[UPLOAD] Local storage created');
       } catch (error) {
-        console.warn('Local upload directory not accessible:', error);
+        console.warn('[UPLOAD] Local upload directory not accessible:', error);
       }
     }
     
@@ -237,9 +323,14 @@ export const GET: APIRoute = async ({ request }) => {
       paths: {
         uploadEndpoint: '/api/upload/media',
         localPath: localStorageAvailable ? '/uploads' : null
+      },
+      environment: {
+        nodeEnv: process.env.NODE_ENV,
+        publicBaseUrl: UPLOAD_CONFIG.publicBaseUrl
       }
     };
     
+    console.log('[UPLOAD] Status check completed:', status);
     return apiResponse.success(status);
     
   }, 'Upload status retrieval failed');
diff --git a/src/pages/contribute/knowledgebase.astro b/src/pages/contribute/knowledgebase.astro
index 1911d83..95874fa 100644
--- a/src/pages/contribute/knowledgebase.astro
+++ b/src/pages/contribute/knowledgebase.astro
@@ -1,5 +1,5 @@
 ---
-// src/pages/contribute/knowledgebase.astro - SIMPLIFIED: Issues only, minimal validation
+// src/pages/contribute/knowledgebase.astro
 import BaseLayout from '../../layouts/BaseLayout.astro';
 import { withAuth } from '../../utils/auth.js';
 import { getToolsData } from '../../utils/dataService.js';
@@ -114,8 +114,13 @@ const sortedTools = data.tools.sort((a: any, b: any) => a.name.localeCompare(b.n
           <div class="form-group">
             <label class="form-label">Dokumente, Bilder, Videos (Optional)</label>
             <div class="upload-area" id="upload-area">
-              <input type="file" id="file-input" multiple accept=".pdf,.doc,.docx,.txt,.md,.zip,.png,.jpg,.jpeg,.gif,.mp4,.webm" class="hidden">
-              <div class="upload-placeholder">
+              <input 
+                type="file" 
+                id="file-input" 
+                multiple 
+                accept=".pdf,.doc,.docx,.txt,.md,.markdown,.csv,.json,.xml,.html,.rtf,.yaml,.yml,.zip,.tar,.gz,.rar,.7z,.png,.jpg,.jpeg,.gif,.webp,.svg,.mp4,.webm,.mov,.avi" 
+                class="hidden"
+              >              <div class="upload-placeholder">
                 <svg width="48" height="48" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="1">
                   <path d="M21 15v4a2 2 0 0 1-2 2H5a2 2 0 0 1-2-2v-4"/>
                   <polyline points="7 10 12 15 17 10"/>
@@ -304,6 +309,12 @@ class KnowledgebaseForm {
 
   private handleFiles(files: File[]) {
     files.forEach(file => {
+      const validation = this.validateFileBeforeUpload(file);
+      if (!validation.valid) {
+        console.log('[UPLOAD]Cannot upload ', file.name, ' Error: ', validation.error);
+        return;
+      }
+
       const fileId = Date.now() + '-' + Math.random().toString(36).substr(2, 9);
       const newFile: UploadedFile = {
         id: fileId,
@@ -317,30 +328,98 @@ class KnowledgebaseForm {
     this.renderFileList();
   }
 
+  private validateFileBeforeUpload(file: File): { valid: boolean; error?: string } {
+    const maxSizeBytes = 50 * 1024 * 1024; // 50MB
+    if (file.size > maxSizeBytes) {
+      const sizeMB = (file.size / 1024 / 1024).toFixed(1);
+      const maxMB = (maxSizeBytes / 1024 / 1024).toFixed(0);
+      return { 
+        valid: false, 
+        error: `File too large (${sizeMB}MB). Maximum size: ${maxMB}MB` 
+      };
+    }
+
+    const allowedExtensions = [
+      '.pdf', '.doc', '.docx', '.txt', '.md', '.markdown', '.csv', '.json', 
+      '.xml', '.html', '.rtf', '.yaml', '.yml', '.zip', '.tar', '.gz', 
+      '.rar', '.7z', '.png', '.jpg', '.jpeg', '.gif', '.webp', '.svg', 
+      '.mp4', '.webm', '.mov', '.avi'
+    ];
+    
+    const fileName = file.name.toLowerCase();
+    const hasValidExtension = allowedExtensions.some(ext => fileName.endsWith(ext));
+    
+    if (!hasValidExtension) {
+      return {
+        valid: false,
+        error: `File type not allowed. Allowed: ${allowedExtensions.join(', ')}`
+      };
+    }
+
+    return { valid: true };
+  }
+
   private async uploadFile(fileId: string) {
     const fileItem = this.uploadedFiles.find(f => f.id === fileId);
-    if (!fileItem) return;
+    if (!fileItem) {
+      console.error('[UPLOAD] File item not found for ID:', fileId);
+      return;
+    }
+
+    console.log('[UPLOAD] Starting upload for:', fileItem.name, 'Size:', fileItem.file.size, 'Type:', fileItem.file.type);
 
     const formData = new FormData();
     formData.append('file', fileItem.file);
     formData.append('type', 'knowledgebase');
 
     try {
+      console.log('[UPLOAD] Sending request to /api/upload/media');
+      
       const response = await fetch('/api/upload/media', {
         method: 'POST',
         body: formData
       });
 
+      console.log('[UPLOAD] Response status:', response.status);
+
+      let responseText: string;
+      let responseData: any;
+      
+      try {
+        responseText = await response.text();
+        console.log('[UPLOAD] Raw response:', responseText.substring(0, 200));
+        
+        try {
+          responseData = JSON.parse(responseText);
+        } catch (parseError) {
+          responseData = { error: responseText };
+        }
+      } catch (readError) {
+        console.error('[UPLOAD] Failed to read response:', readError);
+        throw new Error('Failed to read server response');
+      }
+
       if (response.ok) {
-        const result = await response.json();
+        console.log('[UPLOAD] Success result:', responseData);
+        
         fileItem.uploaded = true;
-        fileItem.url = result.url;
+        fileItem.url = responseData.url;
         this.renderFileList();
+        
       } else {
-        throw new Error('Upload failed');
+        
+        if (responseData && responseData.details) {
+          console.error('[UPLOAD] Error details:', responseData.details);
+        }
+        
       }
     } catch (error) {
-      this.showMessage('error', `Failed to upload ${fileItem.name}`);
+      console.error('[UPLOAD] Upload error for', fileItem.name, ':', error);
+      
+      const errorMessage = error instanceof Error 
+        ? error.message 
+        : 'Unknown upload error';
+      
       this.removeFile(fileId);
     }
   }
@@ -412,7 +491,6 @@ class KnowledgebaseForm {
 
     } catch (error) {
       console.error('[KB FORM] Submission error:', error);
-      this.showMessage('error', 'Submission failed. Please try again.');
     } finally {
       this.isSubmitting = false;
       (this.elements.submitBtn as HTMLButtonElement).disabled = false;
@@ -441,18 +519,6 @@ class KnowledgebaseForm {
     this.renderFileList();
   }
 
-  private showMessage(type: 'success' | 'error' | 'warning', message: string) {
-    const container = document.getElementById('message-container');
-    if (!container) return;
-
-    const messageEl = document.createElement('div');
-    messageEl.className = `message message-${type}`;
-    messageEl.textContent = message;
-
-    container.appendChild(messageEl);
-    setTimeout(() => messageEl.remove(), 5000);
-  }
-
   public removeFileById(fileId: string) {
     this.removeFile(fileId);
   }
diff --git a/src/pages/contribute/tool.astro b/src/pages/contribute/tool.astro
index 1b6babc..1e80526 100644
--- a/src/pages/contribute/tool.astro
+++ b/src/pages/contribute/tool.astro
@@ -22,6 +22,17 @@ const existingTools = data.tools;
 const editToolName = Astro.url.searchParams.get('edit');
 const editTool = editToolName ? existingTools.find(tool => tool.name === editToolName) : null;
 const isEdit = !!editTool;
+
+// Extract data for autocomplete
+const allTags = [...new Set(existingTools.flatMap(tool => tool.tags || []))].sort();
+const allSoftwareAndMethods = existingTools
+  .filter(tool => tool.type === 'software' || tool.type === 'method')
+  .map(tool => tool.name)
+  .sort();
+const allConcepts = existingTools
+  .filter(tool => tool.type === 'concept')
+  .map(tool => tool.name)
+  .sort();
 ---
 
 <BaseLayout title={isEdit ? `Edit ${editTool?.name}` : 'Contribute Tool'}>
@@ -194,16 +205,27 @@ const isEdit = !!editTool;
           </div>
         </div>
 
-        <div id="concepts-fields" style="border: 1px solid var(--color-border); border-radius: 0.5rem; padding: 1.5rem; margin-bottom: 2rem; display: none;">
-          <h3 style="margin: 0 0 1.5rem 0; color: var(--color-primary); border-bottom: 1px solid var(--color-border); padding-bottom: 0.5rem;">Konzepte im Zusammenhang</h3>
-          <div style="display: grid; grid-template-columns: repeat(auto-fit, minmax(200px, 1fr)); gap: 0.5rem;">
-            {existingTools.filter(tool => tool.type === 'concept').map(concept => (
-              <label class="checkbox-wrapper">
-                <input type="checkbox" name="relatedConcepts" value={concept.name} 
-                       checked={editTool?.related_concepts?.includes(concept.name)} />
-                <span>{concept.name}</span>
-              </label>
-            ))}
+        <div id="relations-fields" style="border: 1px solid var(--color-border); border-radius: 0.5rem; padding: 1.5rem; margin-bottom: 2rem; display: none;">
+          <h3 style="margin: 0 0 1.5rem 0; color: var(--color-primary); border-bottom: 1px solid var(--color-border); padding-bottom: 0.5rem;">Verwandte Tools & Konzepte</h3>
+          
+          <div style="display: grid; gap: 1.5rem;">
+            <div id="related-concepts-section">
+              <label for="related-concepts-input" style="display: block; margin-bottom: 0.5rem; font-weight: 600;">Verwandte Konzepte</label>
+              <input type="text" id="related-concepts-input" placeholder="Beginne zu tippen, um Konzepte zu finden..." />
+              <input type="hidden" id="related-concepts-hidden" name="relatedConcepts" value={editTool?.related_concepts?.join(', ') || ''} />
+              <small style="display: block; margin-top: 0.25rem; color: var(--color-text-secondary); font-size: 0.8125rem;">
+                Konzepte, die mit diesem Tool/Methode in Verbindung stehen
+              </small>
+            </div>
+            
+            <div id="related-software-section">
+              <label for="related-software-input" style="display: block; margin-bottom: 0.5rem; font-weight: 600;">Verwandte Software & Methoden</label>
+              <input type="text" id="related-software-input" placeholder="Beginne zu tippen, um Software/Methoden zu finden..." />
+              <input type="hidden" id="related-software-hidden" name="relatedSoftware" value={editTool?.related_software?.join(', ') || ''} />
+              <small style="display: block; margin-top: 0.25rem; color: var(--color-text-secondary); font-size: 0.8125rem;">
+                Software oder Methoden, die oft zusammen mit diesem Tool verwendet werden
+              </small>
+            </div>
           </div>
         </div>
 
@@ -211,9 +233,12 @@ const isEdit = !!editTool;
           <h3 style="margin: 0 0 1.5rem 0; color: var(--color-primary); border-bottom: 1px solid var(--color-border); padding-bottom: 0.5rem;">Zusatzinfos</h3>
           
           <div style="margin-bottom: 1.5rem;">
-            <label for="tags" style="display: block; margin-bottom: 0.5rem; font-weight: 600;">Tags</label>
-            <input type="text" id="tags" name="tags" value={editTool?.tags?.join(', ') || ''} 
-                   placeholder="Komma-getrennt: Passende Begriffe, nach denen ihr suchen würdet." />
+            <label for="tags-input" style="display: block; margin-bottom: 0.5rem; font-weight: 600;">Tags</label>
+            <input type="text" id="tags-input" placeholder="Beginne zu tippen, um Tags hinzuzufügen..." />
+            <input type="hidden" id="tags-hidden" name="tags" value={editTool?.tags?.join(', ') || ''} />
+            <small style="display: block; margin-top: 0.25rem; color: var(--color-text-secondary); font-size: 0.8125rem;">
+              Passende Begriffe, nach denen ihr suchen würdet
+            </small>
           </div>
 
           <div style="margin-bottom: 1.5rem;">
@@ -274,7 +299,275 @@ const isEdit = !!editTool;
   </div>
 </BaseLayout>
 
-<script define:vars={{ isEdit, editTool, domains, phases, domainAgnosticSoftware }}>
+<script define:vars={{ isEdit, editTool, domains, phases, domainAgnosticSoftware, allTags, allSoftwareAndMethods, allConcepts }}>
+// Consolidated Autocomplete Functionality - inlined to avoid module loading issues
+class AutocompleteManager {
+  constructor(inputElement, dataSource, options = {}) {
+    this.input = inputElement;
+    this.dataSource = dataSource;
+    this.options = {
+      minLength: 1,
+      maxResults: 10,
+      placeholder: 'Type to search...',
+      allowMultiple: false,
+      separator: ', ',
+      filterFunction: this.defaultFilter.bind(this),
+      renderFunction: this.defaultRender.bind(this),
+      ...options
+    };
+    
+    this.isOpen = false;
+    this.selectedIndex = -1;
+    this.filteredData = [];
+    this.selectedItems = new Set();
+    
+    this.init();
+  }
+  
+  init() {
+    this.createDropdown();
+    this.bindEvents();
+    
+    if (this.options.allowMultiple) {
+      this.initMultipleMode();
+    }
+  }
+  
+  createDropdown() {
+    this.dropdown = document.createElement('div');
+    this.dropdown.className = 'autocomplete-dropdown';
+    
+    // Insert dropdown after input
+    this.input.parentNode.style.position = 'relative';
+    this.input.parentNode.insertBefore(this.dropdown, this.input.nextSibling);
+  }
+  
+  bindEvents() {
+    this.input.addEventListener('input', (e) => {
+      this.handleInput(e.target.value);
+    });
+    
+    this.input.addEventListener('keydown', (e) => {
+      this.handleKeydown(e);
+    });
+    
+    this.input.addEventListener('focus', () => {
+      if (this.input.value.length >= this.options.minLength) {
+        this.showDropdown();
+      }
+    });
+    
+    this.input.addEventListener('blur', (e) => {
+      // Delay to allow click events on dropdown items
+      setTimeout(() => {
+        if (!this.dropdown.contains(document.activeElement)) {
+          this.hideDropdown();
+        }
+      }, 150);
+    });
+    
+    document.addEventListener('click', (e) => {
+      if (!this.input.contains(e.target) && !this.dropdown.contains(e.target)) {
+        this.hideDropdown();
+      }
+    });
+  }
+  
+  initMultipleMode() {
+    this.selectedContainer = document.createElement('div');
+    this.selectedContainer.className = 'autocomplete-selected';
+    
+    this.input.parentNode.insertBefore(this.selectedContainer, this.input);
+    this.updateSelectedDisplay();
+  }
+  
+  handleInput(value) {
+    if (value.length >= this.options.minLength) {
+      this.filteredData = this.options.filterFunction(value);
+      this.selectedIndex = -1;
+      this.renderDropdown();
+      this.showDropdown();
+    } else {
+      this.hideDropdown();
+    }
+  }
+  
+  handleKeydown(e) {
+    if (!this.isOpen) return;
+    
+    switch (e.key) {
+      case 'ArrowDown':
+        e.preventDefault();
+        this.selectedIndex = Math.min(this.selectedIndex + 1, this.filteredData.length - 1);
+        this.updateHighlight();
+        break;
+        
+      case 'ArrowUp':
+        e.preventDefault();
+        this.selectedIndex = Math.max(this.selectedIndex - 1, -1);
+        this.updateHighlight();
+        break;
+        
+      case 'Enter':
+        e.preventDefault();
+        if (this.selectedIndex >= 0) {
+          this.selectItem(this.filteredData[this.selectedIndex]);
+        }
+        break;
+        
+      case 'Escape':
+        this.hideDropdown();
+        break;
+    }
+  }
+  
+  defaultFilter(query) {
+    const searchTerm = query.toLowerCase();
+    return this.dataSource
+      .filter(item => {
+        const text = typeof item === 'string' ? item : item.name || item.label || item.toString();
+        return text.toLowerCase().includes(searchTerm) && 
+               (!this.options.allowMultiple || !this.selectedItems.has(text));
+      })
+      .slice(0, this.options.maxResults);
+  }
+  
+  defaultRender(item) {
+    const text = typeof item === 'string' ? item : item.name || item.label || item.toString();
+    return `<div class="autocomplete-item">${this.escapeHtml(text)}</div>`;
+  }
+  
+  renderDropdown() {
+    if (this.filteredData.length === 0) {
+      this.dropdown.innerHTML = '<div class="autocomplete-no-results">No results found</div>';
+      return;
+    }
+    
+    this.dropdown.innerHTML = this.filteredData
+      .map((item, index) => {
+        const content = this.options.renderFunction(item);
+        return `<div class="autocomplete-option" data-index="${index}">${content}</div>`;
+      })
+      .join('');
+    
+    // Bind click events
+    this.dropdown.querySelectorAll('.autocomplete-option').forEach((option, index) => {
+      option.addEventListener('click', () => {
+        this.selectItem(this.filteredData[index]);
+      });
+      
+      option.addEventListener('mouseenter', () => {
+        this.selectedIndex = index;
+        this.updateHighlight();
+      });
+    });
+  }
+  
+  updateHighlight() {
+    this.dropdown.querySelectorAll('.autocomplete-option').forEach((option, index) => {
+      option.style.backgroundColor = index === this.selectedIndex 
+        ? 'var(--color-bg-secondary)' 
+        : 'transparent';
+    });
+  }
+  
+  selectItem(item) {
+    const text = typeof item === 'string' ? item : item.name || item.label || item.toString();
+    
+    if (this.options.allowMultiple) {
+      this.selectedItems.add(text);
+      this.updateSelectedDisplay();
+      this.updateInputValue();
+      this.input.value = '';
+    } else {
+      this.input.value = text;
+      this.hideDropdown();
+    }
+    
+    // Trigger change event
+    this.input.dispatchEvent(new CustomEvent('autocomplete:select', {
+      detail: { item, text, selectedItems: Array.from(this.selectedItems) }
+    }));
+  }
+  
+  removeItem(text) {
+    if (this.options.allowMultiple) {
+      this.selectedItems.delete(text);
+      this.updateSelectedDisplay();
+      this.updateInputValue();
+    }
+  }
+  
+  updateSelectedDisplay() {
+    if (!this.options.allowMultiple || !this.selectedContainer) return;
+    
+    this.selectedContainer.innerHTML = Array.from(this.selectedItems)
+      .map(item => `
+        <span class="autocomplete-tag">
+          ${this.escapeHtml(item)}
+          <button type="button" class="autocomplete-remove" data-item="${this.escapeHtml(item)}">×</button>
+        </span>
+      `)
+      .join('');
+    
+    // Bind remove events
+    this.selectedContainer.querySelectorAll('.autocomplete-remove').forEach(btn => {
+      btn.addEventListener('click', (e) => {
+        e.preventDefault();
+        this.removeItem(btn.getAttribute('data-item'));
+      });
+    });
+  }
+  
+  updateInputValue() {
+    if (this.options.allowMultiple && this.options.hiddenInput) {
+      this.options.hiddenInput.value = Array.from(this.selectedItems).join(this.options.separator);
+    }
+  }
+  
+  showDropdown() {
+    this.dropdown.style.display = 'block';
+    this.isOpen = true;
+  }
+  
+  hideDropdown() {
+    this.dropdown.style.display = 'none';
+    this.isOpen = false;
+    this.selectedIndex = -1;
+  }
+  
+  escapeHtml(text) {
+    const div = document.createElement('div');
+    div.textContent = text;
+    return div.innerHTML;
+  }
+  
+  setDataSource(newDataSource) {
+    this.dataSource = newDataSource;
+  }
+  
+  getSelectedItems() {
+    return Array.from(this.selectedItems);
+  }
+  
+  setSelectedItems(items) {
+    this.selectedItems = new Set(items);
+    if (this.options.allowMultiple) {
+      this.updateSelectedDisplay();
+      this.updateInputValue();
+    }
+  }
+  
+  destroy() {
+    if (this.dropdown && this.dropdown.parentNode) {
+      this.dropdown.parentNode.removeChild(this.dropdown);
+    }
+    if (this.selectedContainer && this.selectedContainer.parentNode) {
+      this.selectedContainer.parentNode.removeChild(this.selectedContainer);
+    }
+  }
+}
+
 console.log('[FORM] Script loaded, initializing...');
 
 class ContributionForm {
@@ -282,7 +575,8 @@ class ContributionForm {
     this.isEdit = isEdit;
     this.editTool = editTool;
     this.elements = {};
-    this.isSubmitting = false; 
+    this.isSubmitting = false;
+    this.autocompleteManagers = new Map();
     this.init();
   }
 
@@ -303,14 +597,20 @@ class ContributionForm {
       yamlPreview: document.getElementById('yaml-preview'),
       successModal: document.getElementById('success-modal'),
       softwareFields: document.getElementById('software-fields'),
-      conceptsFields: document.getElementById('concepts-fields'),
+      relationsFields: document.getElementById('relations-fields'),
       descriptionCount: document.getElementById('description-count'),
       reasonCount: document.getElementById('reason-count'),
       validationErrors: document.getElementById('validation-errors'),
       errorList: document.getElementById('error-list'),
       platformsRequired: document.getElementById('platforms-required'),
       licenseRequired: document.getElementById('license-required'),
-      licenseInput: document.getElementById('license')
+      licenseInput: document.getElementById('license'),
+      tagsInput: document.getElementById('tags-input'),
+      tagsHidden: document.getElementById('tags-hidden'),
+      relatedConceptsInput: document.getElementById('related-concepts-input'),
+      relatedConceptsHidden: document.getElementById('related-concepts-hidden'),
+      relatedSoftwareInput: document.getElementById('related-software-input'),
+      relatedSoftwareHidden: document.getElementById('related-software-hidden')
     };
 
     if (!this.elements.form || !this.elements.submitBtn) {
@@ -327,6 +627,7 @@ class ContributionForm {
 
     console.log('[FORM] Setting up handlers...');
     this.setupEventListeners();
+    this.setupAutocomplete();
     this.updateFieldVisibility();
     this.setupCharacterCounters();
     this.updateYAMLPreview();
@@ -334,6 +635,65 @@ class ContributionForm {
     console.log('[FORM] Initialization complete!');
   }
 
+  setupAutocomplete() {
+    // Tags autocomplete
+    if (this.elements.tagsInput && this.elements.tagsHidden) {
+      const tagsManager = new AutocompleteManager(this.elements.tagsInput, allTags, {
+        allowMultiple: true,
+        hiddenInput: this.elements.tagsHidden,
+        placeholder: 'Beginne zu tippen, um Tags hinzuzufügen...'
+      });
+      
+      // Set initial values if editing
+      if (this.editTool?.tags) {
+        tagsManager.setSelectedItems(this.editTool.tags);
+      }
+      
+      this.autocompleteManagers.set('tags', tagsManager);
+    }
+
+    // Related concepts autocomplete
+    if (this.elements.relatedConceptsInput && this.elements.relatedConceptsHidden) {
+      const conceptsManager = new AutocompleteManager(this.elements.relatedConceptsInput, allConcepts, {
+        allowMultiple: true,
+        hiddenInput: this.elements.relatedConceptsHidden,
+        placeholder: 'Beginne zu tippen, um Konzepte zu finden...'
+      });
+      
+      // Set initial values if editing
+      if (this.editTool?.related_concepts) {
+        conceptsManager.setSelectedItems(this.editTool.related_concepts);
+      }
+      
+      this.autocompleteManagers.set('relatedConcepts', conceptsManager);
+    }
+
+    // Related software autocomplete
+    if (this.elements.relatedSoftwareInput && this.elements.relatedSoftwareHidden) {
+      const softwareManager = new AutocompleteManager(this.elements.relatedSoftwareInput, allSoftwareAndMethods, {
+        allowMultiple: true,
+        hiddenInput: this.elements.relatedSoftwareHidden,
+        placeholder: 'Beginne zu tippen, um Software/Methoden zu finden...'
+      });
+      
+      // Set initial values if editing
+      if (this.editTool?.related_software) {
+        softwareManager.setSelectedItems(this.editTool.related_software);
+      }
+      
+      this.autocompleteManagers.set('relatedSoftware', softwareManager);
+    }
+
+    // Listen for autocomplete changes to update YAML preview
+    Object.values(this.autocompleteManagers).forEach(manager => {
+      if (manager.input) {
+        manager.input.addEventListener('autocomplete:select', () => {
+          this.updateYAMLPreview();
+        });
+      }
+    });
+  }
+
   setupEventListeners() {
     this.elements.typeSelect.addEventListener('change', () => {
       this.updateFieldVisibility();
@@ -363,203 +723,221 @@ class ContributionForm {
     console.log('[FORM] Event listeners attached');
   }
 
-updateFieldVisibility() {
-  const type = this.elements.typeSelect.value;
-  
-  this.elements.softwareFields.style.display = 'none';
-  this.elements.conceptsFields.style.display = 'none';
-  
-  if (this.elements.platformsRequired) this.elements.platformsRequired.style.display = 'none';
-  if (this.elements.licenseRequired) this.elements.licenseRequired.style.display = 'none';
-
-  if (type === 'software') {
-    this.elements.softwareFields.style.display = 'block';
-    this.elements.conceptsFields.style.display = 'block';
-    if (this.elements.platformsRequired) this.elements.platformsRequired.style.display = 'inline';
-    if (this.elements.licenseRequired) this.elements.licenseRequired.style.display = 'inline';
-  } else if (type === 'method') {
-    this.elements.conceptsFields.style.display = 'block';
-  }
-
-  console.log('[FORM] Field visibility updated for type:', type);
-}
-
-setupCharacterCounters() {
-  const counters = [
-    { element: this.elements.descriptionTextarea, counter: this.elements.descriptionCount, max: 1000 },
-    { element: this.elements.reasonTextarea, counter: this.elements.reasonCount, max: 500 }
-  ];
-
-  counters.forEach(({ element, counter, max }) => {
-    if (element && counter) {
-      const updateCounter = () => {
-        const count = element.value.length;
-        counter.textContent = count;
-        counter.style.color = count > max * 0.9 ? 'var(--color-warning)' : 'var(--color-text-secondary)';
-      };
-      
-      element.addEventListener('input', updateCounter);
-      updateCounter();
-    }
-  });
-}
-
-updateYAMLPreview() {
-  if (!this.elements.yamlPreview) return;
-
-  try {
-    const formData = new FormData(this.elements.form);
+  updateFieldVisibility() {
+    const type = this.elements.typeSelect.value;
     
-    const tool = {
-      name: formData.get('name') || 'Tool Name',
-      type: formData.get('type') || 'software',
-      description: formData.get('description') || 'Tool description',
-      domains: formData.getAll('domains'),
-      phases: formData.getAll('phases'),
-      skillLevel: formData.get('skillLevel') || 'intermediate',
-      url: formData.get('url') || 'https://example.com'
-    };
-
-    if (formData.get('icon')) {
-      tool.icon = formData.get('icon');
+    // Only hide/show software-specific fields (platforms, license)
+    // Relations should always be visible since all tool types can have relationships
+    this.elements.softwareFields.style.display = type === 'software' ? 'block' : 'none';
+    
+    // Always show relations - all tool types can have relationships
+    this.elements.relationsFields.style.display = 'block';
+    
+    // Only mark platform/license as required for software
+    if (this.elements.platformsRequired) {
+      this.elements.platformsRequired.style.display = type === 'software' ? 'inline' : 'none';
+    }
+    if (this.elements.licenseRequired) {
+      this.elements.licenseRequired.style.display = type === 'software' ? 'inline' : 'none';
     }
 
-    if (tool.type === 'software') {
-      tool.platforms = formData.getAll('platforms');
-      tool.license = formData.get('license') || 'Unknown';
-      if (formData.get('accessType')) {
-        tool.accessType = formData.get('accessType');
+    // Always show both relation sections - let users decide what's relevant
+    const conceptsSection = document.getElementById('related-concepts-section');
+    const softwareSection = document.getElementById('related-software-section');
+    if (conceptsSection) conceptsSection.style.display = 'block';
+    if (softwareSection) softwareSection.style.display = 'block';
+
+    console.log('[FORM] Updated visibility for type:', type || '(no type selected)');
+  }
+
+  setupCharacterCounters() {
+    const counters = [
+      { element: this.elements.descriptionTextarea, counter: this.elements.descriptionCount, max: 1000 },
+      { element: this.elements.reasonTextarea, counter: this.elements.reasonCount, max: 500 }
+    ];
+
+    counters.forEach(({ element, counter, max }) => {
+      if (element && counter) {
+        const updateCounter = () => {
+          const count = element.value.length;
+          counter.textContent = count;
+          counter.style.color = count > max * 0.9 ? 'var(--color-warning)' : 'var(--color-text-secondary)';
+        };
+        
+        element.addEventListener('input', updateCounter);
+        updateCounter();
       }
-      const domainAgnostic = formData.getAll('domainAgnostic');
-      if (domainAgnostic.length > 0) {
-        tool['domain-agnostic-software'] = domainAgnostic;
-      }
-    }
-
-    if (formData.has('knowledgebase')) {
-      tool.knowledgebase = true;
-    }
-
-    const tags = formData.get('tags');
-    if (tags) {
-      tool.tags = tags.split(',').map(t => t.trim()).filter(Boolean);
-    }
-
-    const relatedConcepts = formData.getAll('relatedConcepts');
-    if (relatedConcepts.length > 0) {
-      tool.related_concepts = relatedConcepts;
-    }
-
-    const yaml = this.generateYAML(tool);
-    this.elements.yamlPreview.textContent = yaml;
-
-  } catch (error) {
-    console.error('[FORM] YAML preview error:', error);
-    this.elements.yamlPreview.textContent = '# Error generating preview';
-  }
-}
-
-generateYAML(tool) {
-  const lines = [];
-  
-  lines.push(`name: "${tool.name}"`);
-  if (tool.icon) lines.push(`icon: "${tool.icon}"`);
-  lines.push(`type: ${tool.type}`);
-  lines.push(`description: "${tool.description}"`);
-  lines.push(`domains: [${tool.domains.map(d => `"${d}"`).join(', ')}]`);
-  lines.push(`phases: [${tool.phases.map(p => `"${p}"`).join(', ')}]`);
-  lines.push(`skillLevel: ${tool.skillLevel}`);
-  lines.push(`url: "${tool.url}"`);
-  
-  if (tool.platforms && tool.platforms.length > 0) {
-    lines.push(`platforms: [${tool.platforms.map(p => `"${p}"`).join(', ')}]`);
-  }
-  if (tool.license) lines.push(`license: "${tool.license}"`);
-  if (tool.accessType) lines.push(`accessType: ${tool.accessType}`);
-  if (tool['domain-agnostic-software']) {
-    lines.push(`domain-agnostic-software: [${tool['domain-agnostic-software'].map(c => `"${c}"`).join(', ')}]`);
-  }
-  if (tool.knowledgebase) lines.push(`knowledgebase: true`);
-  if (tool.tags && tool.tags.length > 0) {
-    lines.push(`tags: [${tool.tags.map(t => `"${t}"`).join(', ')}]`);
-  }
-  if (tool.related_concepts && tool.related_concepts.length > 0) {
-    lines.push(`related_concepts: [${tool.related_concepts.map(c => `"${c}"`).join(', ')}]`);
+    });
   }
 
-  return lines.join('\n');
-}
+  updateYAMLPreview() {
+    if (!this.elements.yamlPreview) return;
 
-validateForm() {
-  const errors = [];
-  const formData = new FormData(this.elements.form);
-
-  const name = formData.get('name')?.trim();
-  if (!name) {
-    errors.push('Tool name is required');
-  }
-
-  const description = formData.get('description')?.trim();
-  if (!description) {
-    errors.push('Description is required');
-  } else if (description.length < 10) {
-    errors.push('Description must be at least 10 characters long');
-  }
-
-  const skillLevel = formData.get('skillLevel');
-  if (!skillLevel) {
-    errors.push('Skill level is required');
-  }
-
-  const type = formData.get('type');
-  if (!type) {
-    errors.push('Type is required');
-  }
-
-  const url = formData.get('url')?.trim();
-  if (!url) {
-    errors.push('Primary URL is required');
-  } else {
     try {
-      new URL(url);
-    } catch {
-      errors.push('Primary URL must be a valid URL');
+      const formData = new FormData(this.elements.form);
+      
+      const tool = {
+        name: formData.get('name') || 'Tool Name',
+        type: formData.get('type') || 'software',
+        description: formData.get('description') || 'Tool description',
+        domains: formData.getAll('domains'),
+        phases: formData.getAll('phases'),
+        skillLevel: formData.get('skillLevel') || 'intermediate',
+        url: formData.get('url') || 'https://example.com'
+      };
+
+      if (formData.get('icon')) {
+        tool.icon = formData.get('icon');
+      }
+
+      if (tool.type === 'software') {
+        tool.platforms = formData.getAll('platforms');
+        tool.license = formData.get('license') || 'Unknown';
+        if (formData.get('accessType')) {
+          tool.accessType = formData.get('accessType');
+        }
+        const domainAgnostic = formData.getAll('domainAgnostic');
+        if (domainAgnostic.length > 0) {
+          tool['domain-agnostic-software'] = domainAgnostic;
+        }
+      }
+
+      if (formData.has('knowledgebase')) {
+        tool.knowledgebase = true;
+      }
+
+      // Handle tags from autocomplete
+      const tagsValue = this.elements.tagsHidden?.value || '';
+      if (tagsValue) {
+        tool.tags = tagsValue.split(',').map(t => t.trim()).filter(Boolean);
+      }
+
+      // Handle related concepts from autocomplete
+      const relatedConceptsValue = this.elements.relatedConceptsHidden?.value || '';
+      if (relatedConceptsValue) {
+        tool.related_concepts = relatedConceptsValue.split(',').map(t => t.trim()).filter(Boolean);
+      }
+
+      // Handle related software from autocomplete
+      const relatedSoftwareValue = this.elements.relatedSoftwareHidden?.value || '';
+      if (relatedSoftwareValue) {
+        tool.related_software = relatedSoftwareValue.split(',').map(t => t.trim()).filter(Boolean);
+      }
+
+      const yaml = this.generateYAML(tool);
+      this.elements.yamlPreview.textContent = yaml;
+
+    } catch (error) {
+      console.error('[FORM] YAML preview error:', error);
+      this.elements.yamlPreview.textContent = '# Error generating preview';
     }
   }
 
-  if (type === 'software') {
-    const platforms = formData.getAll('platforms');
-    if (platforms.length === 0) {
-      errors.push('At least one platform is required for software');
+  generateYAML(tool) {
+    const lines = [];
+    
+    lines.push(`name: "${tool.name}"`);
+    if (tool.icon) lines.push(`icon: "${tool.icon}"`);
+    lines.push(`type: ${tool.type}`);
+    lines.push(`description: "${tool.description}"`);
+    lines.push(`domains: [${tool.domains.map(d => `"${d}"`).join(', ')}]`);
+    lines.push(`phases: [${tool.phases.map(p => `"${p}"`).join(', ')}]`);
+    lines.push(`skillLevel: ${tool.skillLevel}`);
+    lines.push(`url: "${tool.url}"`);
+    
+    if (tool.platforms && tool.platforms.length > 0) {
+      lines.push(`platforms: [${tool.platforms.map(p => `"${p}"`).join(', ')}]`);
+    }
+    if (tool.license) lines.push(`license: "${tool.license}"`);
+    if (tool.accessType) lines.push(`accessType: ${tool.accessType}`);
+    if (tool['domain-agnostic-software']) {
+      lines.push(`domain-agnostic-software: [${tool['domain-agnostic-software'].map(c => `"${c}"`).join(', ')}]`);
+    }
+    if (tool.knowledgebase) lines.push(`knowledgebase: true`);
+    if (tool.tags && tool.tags.length > 0) {
+      lines.push(`tags: [${tool.tags.map(t => `"${t}"`).join(', ')}]`);
+    }
+    if (tool.related_concepts && tool.related_concepts.length > 0) {
+      lines.push(`related_concepts: [${tool.related_concepts.map(c => `"${c}"`).join(', ')}]`);
+    }
+    if (tool.related_software && tool.related_software.length > 0) {
+      lines.push(`related_software: [${tool.related_software.map(s => `"${s}"`).join(', ')}]`);
     }
 
-    const license = formData.get('license')?.trim();
-    if (!license) {
-      errors.push('License is required for software');
+    return lines.join('\n');
+  }
+
+  validateForm() {
+    const errors = [];
+    const formData = new FormData(this.elements.form);
+
+    const name = formData.get('name')?.trim();
+    if (!name) {
+      errors.push('Tool name is required');
     }
+
+    const description = formData.get('description')?.trim();
+    if (!description) {
+      errors.push('Description is required');
+    } else if (description.length < 10) {
+      errors.push('Description must be at least 10 characters long');
+    }
+
+    const skillLevel = formData.get('skillLevel');
+    if (!skillLevel) {
+      errors.push('Skill level is required');
+    }
+
+    const type = formData.get('type');
+    if (!type) {
+      errors.push('Type is required');
+    }
+
+    const url = formData.get('url')?.trim();
+    if (!url) {
+      errors.push('Primary URL is required');
+    } else {
+      try {
+        new URL(url);
+      } catch {
+        errors.push('Primary URL must be a valid URL');
+      }
+    }
+
+    if (type === 'software') {
+      const platforms = formData.getAll('platforms');
+      if (platforms.length === 0) {
+        errors.push('At least one platform is required for software');
+      }
+
+      const license = formData.get('license')?.trim();
+      if (!license) {
+        errors.push('License is required for software');
+      }
+    }
+
+    return errors;
   }
 
-  return errors;
-}
+  showValidationErrors(errors) {
+    if (errors.length === 0) {
+      this.elements.validationErrors.style.display = 'none';
+      return;
+    }
 
-showValidationErrors(errors) {
-  if (errors.length === 0) {
-    this.elements.validationErrors.style.display = 'none';
-    return;
+    this.elements.errorList.innerHTML = '';
+    
+    errors.forEach(error => {
+      const li = document.createElement('li');
+      li.textContent = error;
+      this.elements.errorList.appendChild(li);
+    });
+
+    this.elements.validationErrors.style.display = 'block';
+    
+    this.elements.validationErrors.scrollIntoView({ behavior: 'smooth', block: 'center' });
   }
 
-  this.elements.errorList.innerHTML = '';
-  
-  errors.forEach(error => {
-    const li = document.createElement('li');
-    li.textContent = error;
-    this.elements.errorList.appendChild(li);
-  });
-
-  this.elements.validationErrors.style.display = 'block';
-  
-  this.elements.validationErrors.scrollIntoView({ behavior: 'smooth', block: 'center' });
-}
   async handleSubmit() {
     console.log('[FORM] Submit handler called!');
 
@@ -597,14 +975,32 @@ showValidationErrors(errors) {
           phases: formData.getAll('phases'),
           skillLevel: formData.get('skillLevel'),
           url: formData.get('url'),
-          tags: formData.get('tags') ? 
-            formData.get('tags').split(',').map(t => t.trim()).filter(Boolean) : []
+          tags: []
         },
         metadata: {
-          reason: formData.get('reason') || ''
+          reason: formData.get('reason') || '',
+          contact: formData.get('contact') || ''
         }
       };
 
+      // Handle tags from autocomplete
+      const tagsValue = this.elements.tagsHidden?.value || '';
+      if (tagsValue) {
+        submission.tool.tags = tagsValue.split(',').map(t => t.trim()).filter(Boolean);
+      }
+
+      // Handle related concepts from autocomplete
+      const relatedConceptsValue = this.elements.relatedConceptsHidden?.value || '';
+      if (relatedConceptsValue) {
+        submission.tool.related_concepts = relatedConceptsValue.split(',').map(t => t.trim()).filter(Boolean);
+      }
+
+      // Handle related software from autocomplete
+      const relatedSoftwareValue = this.elements.relatedSoftwareHidden?.value || '';
+      if (relatedSoftwareValue) {
+        submission.tool.related_software = relatedSoftwareValue.split(',').map(t => t.trim()).filter(Boolean);
+      }
+
       if (formData.get('icon')) submission.tool.icon = formData.get('icon');
       if (formData.has('knowledgebase')) submission.tool.knowledgebase = true;
 
@@ -620,13 +1016,6 @@ showValidationErrors(errors) {
         }
       }
 
-      if (submission.tool.type !== 'concept') {
-        const related = formData.getAll('relatedConcepts');
-        if (related.length > 0) {
-          submission.tool.related_concepts = related;
-        }
-      }
-
       console.log('[FORM] Sending submission:', submission);
 
       const response = await fetch('/api/contribute/tool', {
@@ -681,6 +1070,14 @@ showValidationErrors(errors) {
       timeout = setTimeout(() => func.apply(this, args), wait);
     };
   }
+
+  destroy() {
+    // Clean up autocomplete managers
+    this.autocompleteManagers.forEach(manager => {
+      manager.destroy();
+    });
+    this.autocompleteManagers.clear();
+  }
 }
 
 function initializeForm() {
@@ -706,4 +1103,5 @@ if (document.readyState === 'loading') {
 }
 
 console.log('[FORM] Script loaded successfully');
-</script>
\ No newline at end of file
+</script>
+</BaseLayout>
\ No newline at end of file
diff --git a/src/pages/index.astro b/src/pages/index.astro
index b5c8c35..276e923 100644
--- a/src/pages/index.astro
+++ b/src/pages/index.astro
@@ -6,10 +6,18 @@ import ToolMatrix from '../components/ToolMatrix.astro';
 import AIQueryInterface from '../components/AIQueryInterface.astro';
 import TargetedScenarios from '../components/TargetedScenarios.astro';
 import { getToolsData } from '../utils/dataService.js';
+import { withAPIAuth, getAuthRequirementForContext } from '../utils/auth.js';
 
 const data = await getToolsData();
 const tools = data.tools;
 const phases = data.phases;
+
+const aiAuthRequired = getAuthRequirementForContext('ai');
+let aiAuthContext: { authenticated: boolean; userId: string; session?: any; authRequired: boolean; } | null = null;
+
+if (aiAuthRequired) {
+  aiAuthContext = await withAPIAuth(Astro.request, 'ai');
+}
 ---
 
 <BaseLayout title="~/">
@@ -36,17 +44,33 @@ const phases = data.phases;
             </div>
           </div>
           
-          <button id="ai-query-btn" class="btn btn-accent btn-lg ai-primary-btn">
-            <svg width="16" height="16" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2">
-              <path d="M9 11H5a2 2 0 0 0-2 2v7a2 2 0 0 0 2 2h14a2 2 0 0 0 2-2v-7a2 2 0 0 0-2-2h-4"/>
-              <path d="M9 11V7a3 3 0 0 1 6 0v4"/>
-            </svg>
-            KI-Beratung starten
-            <svg width="14" height="14" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" class="ml-2">
-              <line x1="7" y1="17" x2="17" y2="7"/>
-              <polyline points="7,7 17,7 17,17"/>
-            </svg>
-          </button>
+          {aiAuthRequired && !aiAuthContext?.authenticated ? (
+            <div class="ai-auth-required">
+              <button id="ai-login-btn" class="btn btn-accent btn-lg">
+                <svg width="16" height="16" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" style="margin-right: 0.5rem;">
+                  <path d="M15 3h4a2 2 0 0 1 2 2v14a2 2 0 0 1-2 2h-4"/>
+                  <polyline points="10 17 15 12 10 7"/>
+                  <line x1="15" y1="12" x2="3" y2="12"/>
+                </svg>
+                Anmelden für KI-Beratung
+              </button>
+              <p style="margin-top: 0.75rem; font-size: 0.875rem; color: var(--color-text-secondary); text-align: center;">
+                Authentifizierung erforderlich für KI-Features
+              </p>
+            </div>
+          ) : (
+            <button id="ai-query-btn" class="btn btn-accent btn-lg ai-primary-btn">
+              <svg width="16" height="16" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2">
+                <path d="M9 11H5a2 2 0 0 0-2 2v7a2 2 0 0 0 2 2h14a2 2 0 0 0 2-2v-7a2 2 0 0 0-2-2h-4"/>
+                <path d="M9 11V7a3 3 0 0 1 6 0v4"/>
+              </svg>
+              KI-Beratung starten
+              <svg width="14" height="14" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" class="ml-2">
+                <line x1="7" y1="17" x2="17" y2="7"/>
+                <polyline points="7,7 17,7 17,17"/>
+              </svg>
+            </button>
+          )}
           
           <div class="ai-features-mini">
             <span class="badge badge-secondary">Workflow-Empfehlungen</span>
@@ -178,7 +202,39 @@ const phases = data.phases;
     <ToolFilters data={data} />
   </section>
 
-  <AIQueryInterface />
+  {aiAuthRequired && !aiAuthContext?.authenticated ? (
+    <section id="ai-interface" class="ai-interface hidden">
+      <div class="ai-query-section">
+        <div class="content-center-lg">
+          <div class="card" style="text-align: center; padding: 3rem; border-left: 4px solid var(--color-accent);">
+            <div style="margin-bottom: 2rem;">
+              <svg width="64" height="64" viewBox="0 0 24 24" fill="none" stroke="var(--color-accent)" stroke-width="1.5" style="margin: 0 auto;">
+                <path d="M9 11H5a2 2 0 0 0-2 2v7a2 2 0 0 0 2 2h14a2 2 0 0 0 2-2v-7a2 2 0 0 0-2-2h-4"/>
+                <path d="M9 11V7a3 3 0 0 1 6 0v4"/>
+                <circle cx="12" cy="12" r="2"/>
+              </svg>
+            </div>
+            <h2 style="margin-bottom: 1rem; color: var(--color-primary);">Anmeldung erforderlich</h2>
+            <p style="margin-bottom: 2rem; color: var(--color-text-secondary); line-height: 1.6;">
+              Für die Nutzung der KI-Beratung ist eine Authentifizierung erforderlich.
+              Melden Sie sich an, um personalisierten Workflow-Empfehlungen und Tool-Analysen zu erhalten.
+            </p>
+            <a href={`/api/auth/login?returnTo=${encodeURIComponent(Astro.url.toString())}`} 
+               class="btn btn-accent btn-lg">
+              <svg width="16" height="16" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" style="margin-right: 0.5rem;">
+                <path d="M15 3h4a2 2 0 0 1 2 2v14a2 2 0 0 1-2 2h-4"/>
+                <polyline points="10 17 15 12 10 7"/>
+                <line x1="15" y1="12" x2="3" y2="12"/>
+              </svg>
+              Anmelden
+            </a>
+          </div>
+        </div>
+      </div>
+    </section>
+  ) : (
+    <AIQueryInterface />
+  )}
 
   <section id="tools-grid" style="padding-bottom: 2rem;">
     <div class="grid-auto-fit" id="tools-container">
@@ -195,7 +251,7 @@ const phases = data.phases;
   <ToolMatrix data={data} />
 </BaseLayout>
 
-<script define:vars={{ toolsData: data.tools, phases: data.phases }}>
+<script define:vars={{ toolsData: data.tools, phases: data.phases, aiAuthRequired: aiAuthRequired, aiAuthenticated: aiAuthContext?.authenticated }}>
   window.toolsData = toolsData;
   
   window.selectApproach = function(approach) {
@@ -211,14 +267,18 @@ const phases = data.phases;
     const selectedCard = document.querySelector(`.approach-card.${approach}`);
     if (selectedCard) selectedCard.classList.add('selected');
     
+    const methodologySection = document.getElementById('methodology-section');
+    const targetedSection = document.getElementById('targeted-section');
+    
+    if (methodologySection) methodologySection.classList.remove('active');
+    if (targetedSection) targetedSection.classList.remove('active');
+    
     if (approach === 'methodology') {
-      const methodologySection = document.getElementById('methodology-section');
       if (methodologySection) {
         methodologySection.classList.add('active');
         window.scrollToElementById('methodology-section');
       }
     } else if (approach === 'targeted') {
-      const targetedSection = document.getElementById('targeted-section');
       if (targetedSection) {
         targetedSection.classList.add('active');
         window.scrollToElementById('targeted-section');
@@ -238,9 +298,12 @@ const phases = data.phases;
       selectedCard.classList.add('active');
     }
     
-    const existingPhaseButton = document.querySelector(`[data-phase="${phase}"]`);
-    if (existingPhaseButton && !existingPhaseButton.classList.contains('active')) {
-      existingPhaseButton.click();
+    const phaseSelect = document.getElementById('phase-select');
+    if (phaseSelect) {
+      phaseSelect.value = phase;
+      
+      const changeEvent = new Event('change', { bubbles: true });
+      phaseSelect.dispatchEvent(changeEvent);
     }
     
     const gridToggle = document.querySelector('.view-toggle[data-view="grid"]');
@@ -248,7 +311,9 @@ const phases = data.phases;
       gridToggle.click();
     }
     
-    window.scrollToElementById('tools-grid');
+    setTimeout(() => {
+      window.scrollToElementById('tools-grid');
+    }, 200);
   };
 
   document.addEventListener('DOMContentLoaded', () => {
@@ -259,12 +324,20 @@ const phases = data.phases;
     const filtersSection = document.getElementById('filters-section');
     const noResults = document.getElementById('no-results');
     const aiQueryBtn = document.getElementById('ai-query-btn');
+    const aiLoginBtn = document.getElementById('ai-login-btn');
     
     if (!toolsContainer || !toolsGrid || !matrixContainer || !noResults || !aiInterface || !filtersSection) {
       console.error('Required DOM elements not found');
       return;
     }
     
+    if (aiLoginBtn) {
+      aiLoginBtn.addEventListener('click', () => {
+        const currentUrl = encodeURIComponent(window.location.href);
+        window.location.href = `/api/auth/login?returnTo=${currentUrl}`;
+      });
+    }
+    
     if (aiQueryBtn) {
       aiQueryBtn.addEventListener('click', () => {
         aiQueryBtn.classList.add('activated');
@@ -282,59 +355,80 @@ const phases = data.phases;
       });
     }
 
-  function switchToView(view) {
-    const toolsGrid = document.getElementById('tools-grid');
-    const matrixContainer = document.getElementById('matrix-container');
-    const aiInterface = document.getElementById('ai-interface');
-    const filtersSection = document.getElementById('filters-section');
-    const noResults = document.getElementById('no-results');
-    
-    if (toolsGrid) toolsGrid.style.display = 'none';
-    if (matrixContainer) matrixContainer.style.display = 'none';
-    if (aiInterface) aiInterface.style.display = 'none';
-    if (filtersSection) filtersSection.style.display = 'none';
-    if (noResults) noResults.style.display = 'none';
-    
-    switch (view) {
-      case 'grid':
-        if (toolsGrid) toolsGrid.style.display = 'block';
-        if (filtersSection) filtersSection.style.display = 'block';
-        break;
-      case 'matrix':
-        if (matrixContainer) matrixContainer.style.display = 'block';
-        if (filtersSection) filtersSection.style.display = 'block';
-        break;
-      case 'ai':
-        if (aiInterface) aiInterface.style.display = 'block';
-        break;
-    }
-  }
-
-    function hideFilterControls() {
-      const filterSections = document.querySelectorAll('.filter-section');
-      filterSections.forEach((section, index) => {
-        if (index < filterSections.length - 1) {
-          section.style.display = 'none';
-        }
-      });
-    }
-
-    function showFilterControls() {
-      const filterSections = document.querySelectorAll('.filter-section');
-      const searchInput = document.getElementById('search-input');
-      const tagCloud = document.querySelector('.tag-cloud');
-      const tagControls = document.querySelector('.tag-controls');
-      const checkboxWrappers = document.querySelectorAll('.checkbox-wrapper');
-      const allInputs = filtersSection.querySelectorAll('input, select, textarea');
+    function switchToView(view) {
+      console.log('[VIEW] Switching to view:', view);
       
-      filterSections.forEach(section => section.style.display = 'block');
+      const toolsGrid = document.getElementById('tools-grid');
+      const matrixContainer = document.getElementById('matrix-container');
+      const aiInterface = document.getElementById('ai-interface');
+      const filtersSection = document.getElementById('filters-section');
+      const noResults = document.getElementById('no-results');
       
-      if (searchInput) searchInput.style.display = 'block';
-      if (tagCloud) tagCloud.style.display = 'flex';
-      if (tagControls) tagControls.style.display = 'flex';
+      const methodologySection = document.getElementById('methodology-section');
+      const targetedSection = document.getElementById('targeted-section');
       
-      allInputs.forEach(input => input.style.display = 'block');
-      checkboxWrappers.forEach(wrapper => wrapper.style.display = 'flex');
+      if (toolsGrid) toolsGrid.style.display = 'none';
+      if (matrixContainer) {
+        matrixContainer.style.display = 'none';
+        matrixContainer.classList.add('hidden');
+      }
+      if (aiInterface) aiInterface.style.display = 'none';
+      if (noResults) noResults.style.display = 'none';
+      
+      if (methodologySection) methodologySection.classList.remove('active');
+      if (targetedSection) targetedSection.classList.remove('active');
+      
+      switch (view) {
+        case 'grid':
+          console.log('[VIEW] Showing grid view');
+          if (toolsGrid) toolsGrid.style.display = 'block';
+          if (filtersSection) filtersSection.style.display = 'block';
+          break;
+          
+        case 'matrix':
+          console.log('[VIEW] Showing matrix view');
+          if (matrixContainer) {
+            matrixContainer.style.display = 'block';
+            matrixContainer.classList.remove('hidden');
+          }
+          if (filtersSection) filtersSection.style.display = 'block';
+          break;
+          
+        case 'ai':
+          console.log('[VIEW] Showing AI view');
+          if (aiAuthRequired && !aiAuthenticated) {
+            console.log('[AUTH] AI access denied, redirecting to login');
+            const currentUrl = encodeURIComponent(window.location.href);
+            window.location.href = `/api/auth/login?returnTo=${currentUrl}`;
+            return;
+          }
+          
+          if (aiInterface) aiInterface.style.display = 'block';
+          
+          if (filtersSection) {
+            filtersSection.style.display = 'block';
+            
+            const filterSections = filtersSection.querySelectorAll('.filter-section');
+            filterSections.forEach((section, index) => {
+              if (index === filterSections.length - 1) {
+                section.style.display = 'block';
+              } else {
+                section.style.display = 'none';
+              }
+            });
+          }
+          break;
+          
+        default:
+          console.warn('[VIEW] Unknown view:', view);
+      }
+      
+      if (view !== 'ai' && filtersSection) {
+        const filterSections = filtersSection.querySelectorAll('.filter-section');
+        filterSections.forEach(section => {
+          section.style.display = 'block';
+        });
+      }
     }
 
     window.navigateToGrid = function(toolName) {
@@ -416,6 +510,8 @@ const phases = data.phases;
     };
 
     function handleSharedURL() {
+      console.log('[SHARE] Handling shared URL:', window.location.search);
+      
       const urlParams = new URLSearchParams(window.location.search);
       const toolParam = urlParams.get('tool');
       const viewParam = urlParams.get('view');
@@ -427,13 +523,19 @@ const phases = data.phases;
         }
         return;
       }
+            
+      if (!window.findToolByIdentifier) {
+        console.error('[SHARE] findToolByIdentifier not available, retrying...');
+        setTimeout(() => handleSharedURL(), 200);
+        return;
+      }
       
       const tool = window.findToolByIdentifier(window.toolsData, toolParam);
       if (!tool) {
-        console.warn('Shared tool not found:', toolParam);
         return;
       }
       
+      
       const cleanUrl = window.location.protocol + "//" + window.location.host + window.location.pathname;
       window.history.replaceState({}, document.title, cleanUrl);
       
@@ -455,11 +557,11 @@ const phases = data.phases;
           default:
             window.navigateToGrid(tool.name);
         }
-      }, 100);
+      }, 300);
     }
 
     window.addEventListener('toolsFiltered', (event) => {
-      const filtered = event.detail;
+      const { tools: filtered, semanticSearch } = event.detail;
       const currentView = document.querySelector('.view-toggle.active')?.getAttribute('data-view');
       
       if (currentView === 'matrix' || currentView === 'ai') {
@@ -468,33 +570,127 @@ const phases = data.phases;
       
       const allToolCards = document.querySelectorAll('.tool-card');
       const filteredNames = new Set(filtered.map(tool => tool.name.toLowerCase()));
+      const toolsContainer = document.getElementById('tools-container');
       
       let visibleCount = 0;
       
-      allToolCards.forEach(card => {
-        const toolName = card.getAttribute('data-tool-name');
-        if (filteredNames.has(toolName)) {
-          card.style.display = 'block';
-          visibleCount++;
-        } else {
-          card.style.display = 'none';
+      if (semanticSearch && filtered.length > 0) {
+        console.log('[SEMANTIC] Reordering tools by semantic similarity');
+        
+        const orderedCards = [];
+        const remainingCards = [];
+        
+        filtered.forEach(tool => {
+          const toolName = tool.name.toLowerCase();
+          const matchingCard = Array.from(allToolCards).find(card => 
+            card.getAttribute('data-tool-name') === toolName
+          );
+          
+          if (matchingCard) {
+            matchingCard.style.display = 'block';
+            orderedCards.push(matchingCard);
+            visibleCount++;
+            
+            if (tool._semanticSimilarity) {
+              matchingCard.setAttribute('data-semantic-similarity', tool._semanticSimilarity.toFixed(3));
+              matchingCard.setAttribute('data-semantic-rank', tool._semanticRank || '');
+              
+              const header = matchingCard.querySelector('.tool-card-header h3');
+              if (header && tool._semanticRank <= 3) {
+                const existingIndicator = header.querySelector('.semantic-rank-indicator');
+                if (existingIndicator) {
+                  existingIndicator.remove();
+                }
+                
+                const indicator = document.createElement('span');
+                indicator.className = 'semantic-rank-indicator';
+                indicator.style.cssText = `
+                  display: inline-block;
+                  width: 6px;
+                  height: 6px;
+                  background-color: var(--color-accent);
+                  border-radius: 50%;
+                  margin-left: 0.5rem;
+                  opacity: ${1 - (tool._semanticRank - 1) * 0.3};
+                `;
+                indicator.title = `Semantische Relevanz: ${tool._semanticSimilarity.toFixed(3)}`;
+                header.appendChild(indicator);
+              }
+            }
+          }
+        });
+        
+        allToolCards.forEach(card => {
+          const toolName = card.getAttribute('data-tool-name');
+          if (!filteredNames.has(toolName)) {
+            card.style.display = 'none';
+            remainingCards.push(card);
+          }
+        });
+        
+        const allCards = [...orderedCards, ...remainingCards];
+        allCards.forEach(card => {
+          toolsContainer.appendChild(card);
+        });
+        
+      } else {
+        allToolCards.forEach(card => {
+          const toolName = card.getAttribute('data-tool-name');
+          
+          card.removeAttribute('data-semantic-similarity');
+          card.removeAttribute('data-semantic-rank');
+          const semanticIndicator = card.querySelector('.semantic-rank-indicator');
+          if (semanticIndicator) {
+            semanticIndicator.remove();
+          }
+          
+          if (filteredNames.has(toolName)) {
+            card.style.display = 'block';
+            visibleCount++;
+          } else {
+            card.style.display = 'none';
+          }
+        });
+        
+        if (!semanticSearch) {
+          const originalOrder = Array.from(allToolCards).sort((a, b) => {
+            const aIndex = Array.from(allToolCards).indexOf(a);
+            const bIndex = Array.from(allToolCards).indexOf(b);
+            return aIndex - bIndex;
+          });
+          
+          originalOrder.forEach(card => {
+            toolsContainer.appendChild(card);
+          });
         }
-      });
+      }
       
       if (visibleCount === 0) {
         noResults.style.display = 'block';
       } else {
         noResults.style.display = 'none';
       }
+      
+      if (semanticSearch) {
+        console.log(`[SEMANTIC] Displayed ${visibleCount} tools in semantic order`);
+      }
     });
-    
+      
     window.addEventListener('viewChanged', (event) => {
       const view = event.detail;
-      switchToView(view);
+      if (!event.triggeredByButton) {
+        switchToView(view);
+      }
     });
 
     window.switchToAIView = () => switchToView('ai');
+    window.switchToView = switchToView;
 
-    handleSharedURL();
+    // CRITICAL: Handle shared URLs AFTER everything is set up
+    // Increased timeout to ensure all components and utility functions are loaded
+    setTimeout(() => {
+      handleSharedURL();
+    }, 1000);
   });
-</script>
\ No newline at end of file
+</script>
+</BaseLayout>
\ No newline at end of file
diff --git a/src/pages/knowledgebase.astro b/src/pages/knowledgebase.astro
index 6daf17c..1da2f64 100644
--- a/src/pages/knowledgebase.astro
+++ b/src/pages/knowledgebase.astro
@@ -108,13 +108,15 @@ knowledgebaseEntries.sort((a: any, b: any) => a.title.localeCompare(b.title));
             const isConcept = hasAssociatedTool && entry.associatedTool.type === 'concept';
             const isStandalone = !hasAssociatedTool;
             
+            const articleUrl = `/knowledgebase/${entry.slug}`;
+            
             return (
               <article 
                 class="kb-entry card cursor-pointer" 
                 id={`kb-${entry.slug}`} 
                 data-tool-name={entry.title.toLowerCase()}
                 data-article-type={isStandalone ? 'standalone' : 'tool-associated'}
-                onclick={`window.location.href='/knowledgebase/${entry.slug}'`}
+                onclick={`window.location.href='${articleUrl}'`}
               >
                 <!-- Card Header -->
                 <div class="flex-between mb-3">
@@ -137,7 +139,7 @@ knowledgebaseEntries.sort((a: any, b: any) => a.title.localeCompare(b.title));
                   </div>
                   
                   <div class="flex gap-2 flex-shrink-0" onclick="event.stopPropagation();">
-                    <a href={`/knowledgebase/${entry.slug}`} class="btn btn-primary btn-sm">
+                    <a href={articleUrl} class="btn btn-primary btn-sm">
                       <svg width="14" height="14" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" style="margin-right: 0.375rem;">
                         <path d="M18 13v6a2 2 0 0 1-2 2H5a2 2 0 0 1-2-2V8a2 2 0 0 1 2-2h6"/>
                         <polyline points="15 3 21 3 21 9"/>
@@ -145,7 +147,21 @@ knowledgebaseEntries.sort((a: any, b: any) => a.title.localeCompare(b.title));
                       </svg>
                       Öffnen
                     </a>
-                    <ContributionButton type="edit" toolName={entry.tool_name || entry.title} variant="secondary" text="Edit" style="font-size: 0.8125rem; padding: 0.5rem 0.75rem;" />
+                    <button 
+                      class="btn btn-secondary btn-sm"
+                      onclick="window.shareArticle(this, '${articleUrl}', '${entry.title}')"
+                      title="Artikel teilen"
+                      style="font-size: 0.8125rem; padding: 0.5rem 0.75rem;"
+                    >
+                      <svg width="14" height="14" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" style="margin-right: 0.375rem;">
+                        <circle cx="18" cy="5" r="3"/>
+                        <circle cx="6" cy="12" r="3"/>
+                        <circle cx="18" cy="19" r="3"/>
+                        <line x1="8.59" y1="13.51" x2="15.42" y2="17.49"/>
+                        <line x1="15.41" y1="6.51" x2="8.59" y2="10.49"/>
+                      </svg>
+                      Teilen
+                    </button>
                   </div>
                 </div>
 
@@ -283,5 +299,7 @@ knowledgebaseEntries.sort((a: any, b: any) => a.title.localeCompare(b.title));
         lastScrollY = window.scrollY;
       }
     });
+
+
   });
 </script>
\ No newline at end of file
diff --git a/src/pages/knowledgebase/[slug].astro b/src/pages/knowledgebase/[slug].astro
index 312bce7..fb47cb0 100644
--- a/src/pages/knowledgebase/[slug].astro
+++ b/src/pages/knowledgebase/[slug].astro
@@ -47,192 +47,435 @@ const hasValidProjectUrl = displayTool && displayTool.projectUrl !== undefined &
                           displayTool.projectUrl !== null && 
                           displayTool.projectUrl !== "" && 
                           displayTool.projectUrl.trim() !== "";
+
+const currentUrl = Astro.url.href;
 ---
 
 <BaseLayout title={entry.data.title} description={entry.data.description}>
-  <article style="max-width: 900px; margin: 0 auto;">
-    <header style="margin-bottom: 2rem; padding: 2rem; background: linear-gradient(135deg, var(--color-bg-secondary) 0%, var(--color-bg-tertiary) 100%); border-radius: 1rem; border: 1px solid var(--color-border);">
-      <div style="display: flex; justify-content: space-between; align-items: start; margin-bottom: 1rem;">
-        <div style="flex: 1;">
-          <h1 style="margin: 0 0 0.5rem 0; color: var(--color-primary);">
-            {displayTool?.icon && <span style="margin-right: 0.75rem; font-size: 1.5rem;">{displayTool.icon}</span>}
+  <div class="article-layout">
+    <!-- Article Header -->
+    <header class="article-header">
+      <div class="article-header-content">
+        <div class="article-meta">
+          <nav class="breadcrumb">
+            <a href="/knowledgebase" class="breadcrumb-link">Knowledgebase</a>
+            <span class="breadcrumb-separator">→</span>
+            <span class="breadcrumb-current">{entry.data.title}</span>
+          </nav>
+          
+          <div class="article-tags">
+            {entry.data.categories?.map((cat: string) => (
+              <span class="article-tag article-tag-category">{cat}</span>
+            ))}
+            {entry.data.tags?.map((tag: string) => (
+              <span class="article-tag">{tag}</span>
+            ))}
+          </div>
+        </div>
+        
+        <div class="article-title-section">
+          <h1 class="article-title">
+            {displayTool?.icon && <span class="article-icon">{displayTool.icon}</span>}
             {entry.data.title}
           </h1>
-          <p style="margin: 0; color: var(--color-text-secondary); font-size: 1.125rem;">
-            {entry.data.description}
-          </p>
-        </div>
-        <div style="display: flex; flex-direction: column; gap: 0.5rem; align-items: end;">
-          <div style="display: flex; gap: 0.5rem; flex-wrap: wrap;">
-            {isStandalone ? (
-              <span class="badge" style="background-color: var(--color-accent); color: white;">Artikel</span>
-            ) : (
-              <>
-                {isConcept && <span class="badge" style="background-color: var(--color-concept); color: white;">Konzept</span>}
-                {isMethod && <span class="badge" style="background-color: var(--color-method); color: white;">Methode</span>}
-                {!isMethod && !isConcept && !isStandalone && <span class="badge" style="background-color: var(--color-primary); color: white;">Software</span>}
-                {!isMethod && !isConcept && hasValidProjectUrl && <span class="badge badge-primary">CC24-Server</span>}
-                {!isMethod && !isConcept && !isStandalone && displayTool?.license !== 'Proprietary' && <span class="badge badge-success">Open Source</span>}
-              </>
-            )}
-            <span class="badge badge-error">📖</span>
-          </div>
-        </div>
-      </div>
-      
-      <div style="display: grid; grid-template-columns: repeat(auto-fit, minmax(200px, 1fr)); gap: 1rem; margin-top: 1rem; padding-top: 1rem; border-top: 1px solid var(--color-border);">
-        {entry.data.difficulty && (
-          <div>
-            <strong style="font-size: 0.875rem; color: var(--color-text-secondary);">Schwierigkeit</strong>
-            <p style="margin: 0; font-size: 0.9375rem;">{entry.data.difficulty}</p>
-          </div>
-        )}
-        
-        <div>
-          <strong style="font-size: 0.875rem; color: var(--color-text-secondary);">Letztes Update</strong>
-          <p style="margin: 0; font-size: 0.9375rem;">{entry.data.last_updated.toLocaleDateString('de-DE')}</p>
+          <p class="article-description">{entry.data.description}</p>
         </div>
         
-        <div>
-          <strong style="font-size: 0.875rem; color: var(--color-text-secondary);">Autor</strong>
-          <p style="margin: 0; font-size: 0.9375rem;">{entry.data.author}</p>
-        </div>
-        
-        <div>
-          <strong style="font-size: 0.875rem; color: var(--color-text-secondary);">Typ</strong>
-          <p style="margin: 0; font-size: 0.9375rem;">
-            {isStandalone ? 'Allgemeiner Artikel' : 
-             isConcept ? 'Konzept-Artikel' : 
-             isMethod ? 'Methoden-Artikel' : 
-             'Software-Artikel'}
-          </p>
-        </div>
-        
-        {entry.data.categories && entry.data.categories.length > 0 && (
-          <div style="grid-column: 1 / -1;">
-            <strong style="font-size: 0.875rem; color: var(--color-text-secondary);">Kategorien</strong>
-            <div style="display: flex; flex-wrap: wrap; gap: 0.25rem; margin-top: 0.25rem;">
-              {entry.data.categories.map((cat: string) => (
-                <span class="tag" style="font-size: 0.75rem;">{cat}</span>
-              ))}
+        <div class="article-metadata-grid">
+          <div class="metadata-item">
+            <span class="metadata-label">Typ</span>
+            <div class="metadata-badges">
+              {isStandalone ? (
+                <span class="badge badge-accent">Artikel</span>
+              ) : (
+                <>
+                  {isConcept && <span class="badge badge-concept">Konzept</span>}
+                  {isMethod && <span class="badge badge-method">Methode</span>}
+                  {!isMethod && !isConcept && <span class="badge badge-primary">Software</span>}
+                  {hasValidProjectUrl && <span class="badge badge-primary">CC24-Server</span>}
+                </>
+              )}
+              <span class="badge badge-error">📖</span>
             </div>
           </div>
-        )}
+          
+          {entry.data.difficulty && (
+            <div class="metadata-item">
+              <span class="metadata-label">Schwierigkeit</span>
+              <span class="metadata-value">{entry.data.difficulty}</span>
+            </div>
+          )}
+          
+          <div class="metadata-item">
+            <span class="metadata-label">Aktualisiert</span>
+            <span class="metadata-value">{entry.data.last_updated.toLocaleDateString('de-DE')}</span>
+          </div>
+          
+          <div class="metadata-item">
+            <span class="metadata-label">Autor</span>
+            <span class="metadata-value">{entry.data.author}</span>
+          </div>
+          
+          <div class="metadata-item">
+            <span class="metadata-label">Lesezeit</span>
+            <span class="metadata-value" id="reading-time">~5 min</span>
+          </div>
+        </div>
+        
+        <div class="article-actions">
+          <button 
+            id="share-article-btn"
+            class="btn btn-secondary"
+            onclick="window.shareCurrentArticle(this)"
+            title="Artikel teilen"
+          >
+            <svg width="16" height="16" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2">
+              <circle cx="18" cy="5" r="3"/>
+              <circle cx="6" cy="12" r="3"/>
+              <circle cx="18" cy="19" r="3"/>
+              <line x1="8.59" y1="13.51" x2="15.42" y2="17.49"/>
+              <line x1="15.41" y1="6.51" x2="8.59" y2="10.49"/>
+            </svg>
+            Teilen
+          </button>
+          
+          <a href="/knowledgebase" class="btn btn-secondary">
+            <svg width="16" height="16" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2">
+              <polyline points="15,18 9,12 15,6"></polyline>
+            </svg>
+            Zurück
+          </a>
+        </div>
       </div>
     </header>
 
-    <nav style="margin-bottom: 2rem; position: relative; z-index: 50;">
-      <a href="/knowledgebase" class="btn btn-secondary">
-        <svg width="16" height="16" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" style="margin-right: 0.5rem;">
-          <polyline points="15,18 9,12 15,6"></polyline>
-        </svg>
-        Zurück zur Knowledgebase
-      </a>
-    </nav>
-
-    <div class="card" style="padding: 2rem;">
-      <div class="kb-content markdown-content" style="line-height: 1.7;">
-        <Content />
-      </div>
-    </div>
-
-    <div class="card" style="margin-top: 2rem; background-color: var(--color-bg-secondary);">
-      <h3 style="margin: 0 0 1rem 0; color: var(--color-text);">
-        {isStandalone ? 'Verwandte Aktionen' : 'Tool-Aktionen'}
-      </h3>
+    <!-- Main Content Area -->
+    <div class="article-content-wrapper">
+      <!-- Sidebar Navigation (will be populated by JS) -->
+      <aside class="article-sidebar">
+        <!-- TOC will be inserted here by JavaScript -->
+      </aside>
       
-      <div style="display: flex; gap: 1rem; flex-wrap: wrap;">
-        {isStandalone ? (
-          <a href="/knowledgebase" class="btn btn-primary">
-            <svg width="16" height="16" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" style="margin-right: 0.5rem;">
-              <path d="M14 2H6a2 2 0 0 0-2 2v16a2 2 0 0 0 2 2h12a2 2 0 0 0 2-2V8z"/>
-              <polyline points="14 2 14 8 20 8"/>
-              <line x1="16" y1="13" x2="8" y2="13"/>
-              <line x1="16" y1="17" x2="8" y2="17"/>
-              <polyline points="10 9 9 9 8 9"/>
-            </svg>
-            Weitere Artikel
-          </a>
-        ) : (
-          <>
-            {isConcept ? (
-              <a href={displayTool.url} target="_blank" rel="noopener noreferrer" class="btn btn-primary" style="background-color: var(--color-concept); border-color: var(--color-concept);">
-                <svg width="16" height="16" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" style="margin-right: 0.5rem;">
-                  <path d="M18 13v6a2 2 0 0 1-2 2H5a2 2 0 0 1-2-2V8a2 2 0 0 1 2-2h6"/>
-                  <polyline points="15 3 21 3 21 9"/>
-                  <line x1="10" y1="14" x2="21" y2="3"/>
-                </svg>
-                Mehr erfahren
-              </a>
-            ) : isMethod ? (
-              <a href={displayTool.projectUrl || displayTool.url} target="_blank" rel="noopener noreferrer" class="btn btn-primary" style="background-color: var(--color-method); border-color: var(--color-method);">
-                <svg width="16" height="16" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" style="margin-right: 0.5rem;">
-                  <path d="M18 13v6a2 2 0 0 1-2 2H5a2 2 0 0 1-2-2V8a2 2 0 0 1 2-2h6"/>
-                  <polyline points="15 3 21 3 21 9"/>
-                  <line x1="10" y1="14" x2="21" y2="3"/>
-                </svg>
-                Zur Methode
-              </a>
-            ) : (
-              <>
-                <a href={displayTool.url} target="_blank" rel="noopener noreferrer" class="btn btn-secondary">
-                  <svg width="16" height="16" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" style="margin-right: 0.5rem;">
-                    <path d="M18 13v6a2 2 0 0 1-2 2H5a2 2 0 0 1-2-2V8a2 2 0 0 1 2-2h6"/>
-                    <polyline points="15 3 21 3 21 9"/>
-                    <line x1="10" y1="14" x2="21" y2="3"/>
-                  </svg>
-                  Software-Homepage
-                </a>
-                {hasValidProjectUrl && (
-                  <a href={displayTool.projectUrl} target="_blank" rel="noopener noreferrer" class="btn btn-primary">
-                    <svg width="16" height="16" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" style="margin-right: 0.5rem;">
-                      <circle cx="12" cy="12" r="10"/>
-                      <path d="M12 16l4-4-4-4"/>
-                      <path d="M8 12h8"/>
-                    </svg>
-                    Zugreifen
-                  </a>
-                )}
-              </>
-            )}
-          </>
-        )}
+      <!-- Article Content -->
+      <main class="article-main">
+        <article class="article-content">
+          <div class="markdown-content">
+            <Content />
+          </div>
+        </article>
         
-        {relatedTools.length > 0 && relatedTools.length > (primaryTool ? 1 : 0) && (
-          <div style="margin-left: auto;">
-            <details style="position: relative;">
-              <summary class="btn btn-secondary" style="cursor: pointer; list-style: none;">
-                <svg width="16" height="16" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" style="margin-right: 0.5rem;">
-                  <path d="M16 21v-2a4 4 0 0 0-4-4H5a4 4 0 0 0-4 4v2"/>
-                  <circle cx="8.5" cy="7" r="4"/>
-                  <line x1="20" y1="8" x2="20" y2="14"/>
-                  <line x1="23" y1="11" x2="17" y2="11"/>
+        <!-- Article Footer -->
+        <footer class="article-footer">
+          <div class="article-footer-actions">
+            <h3>Tool-Aktionen</h3>
+            <div class="footer-actions-grid">
+              {isStandalone ? (
+                <a href="/knowledgebase" class="btn btn-primary">
+                  <svg width="16" height="16" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2">
+                    <path d="M14 2H6a2 2 0 0 0-2 2v16a2 2 0 0 0 2 2h12a2 2 0 0 0 2-2V8z"/>
+                    <polyline points="14 2 14 8 20 8"/>
+                  </svg>
+                  Weitere Artikel
+                </a>
+              ) : (
+                <>
+                  {isConcept ? (
+                    <a href={displayTool.url} target="_blank" rel="noopener noreferrer" class="btn btn-concept">
+                      <svg width="16" height="16" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2">
+                        <path d="M18 13v6a2 2 0 0 1-2 2H5a2 2 0 0 1-2-2V8a2 2 0 0 1 2-2h6"/>
+                        <polyline points="15 3 21 3 21 9"/>
+                        <line x1="10" y1="14" x2="21" y2="3"/>
+                      </svg>
+                      Mehr erfahren
+                    </a>
+                  ) : isMethod ? (
+                    <a href={displayTool.projectUrl || displayTool.url} target="_blank" rel="noopener noreferrer" class="btn btn-method">
+                      <svg width="16" height="16" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2">
+                        <path d="M18 13v6a2 2 0 0 1-2 2H5a2 2 0 0 1-2-2V8a2 2 0 0 1 2-2h6"/>
+                        <polyline points="15 3 21 3 21 9"/>
+                        <line x1="10" y1="14" x2="21" y2="3"/>
+                      </svg>
+                      Zur Methode
+                    </a>
+                  ) : (
+                    <>
+                      <a href={displayTool.url} target="_blank" rel="noopener noreferrer" class="btn btn-secondary">
+                        <svg width="16" height="16" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2">
+                          <path d="M18 13v6a2 2 0 0 1-2 2H5a2 2 0 0 1-2-2V8a2 2 0 0 1 2-2h6"/>
+                          <polyline points="15 3 21 3 21 9"/>
+                          <line x1="10" y1="14" x2="21" y2="3"/>
+                        </svg>
+                        Homepage
+                      </a>
+                      {hasValidProjectUrl && (
+                        <a href={displayTool.projectUrl} target="_blank" rel="noopener noreferrer" class="btn btn-primary">
+                          <svg width="16" height="16" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2">
+                            <circle cx="12" cy="12" r="10"/>
+                            <path d="M12 16l4-4-4-4"/>
+                            <path d="M8 12h8"/>
+                          </svg>
+                          Zugreifen
+                        </a>
+                      )}
+                    </>
+                  )}
+                </>
+              )}
+              
+              <a href="/" class="btn btn-secondary">
+                <svg width="16" height="16" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2">
+                  <path d="M3 9l9-7 9 7v11a2 2 0 0 1-2 2H5a2 2 0 0 1-2-2z"/>
+                  <polyline points="9,22 9,12 15,12 15,22"/>
                 </svg>
-                Verwandte Tools ({relatedTools.length})
-              </summary>
-              <div style="position: absolute; top: 100%; left: 0; background: var(--color-bg); border: 1px solid var(--color-border); border-radius: 0.5rem; padding: 1rem; min-width: 200px; z-index: 100; box-shadow: var(--shadow-lg);">
+                Zur Hauptseite
+              </a>
+            </div>
+          </div>
+          
+          {relatedTools.length > 0 && (
+            <div class="related-tools">
+              <h3>Verwandte Tools</h3>
+              <div class="related-tools-grid">
                 {relatedTools.map((tool: any) => (
-                  <a href={tool.projectUrl || tool.url} target="_blank" rel="noopener noreferrer" 
-                     style="display: block; padding: 0.5rem; border-radius: 0.25rem; text-decoration: none; color: var(--color-text); margin-bottom: 0.25rem;"
-                     onmouseover="this.style.backgroundColor='var(--color-bg-secondary)'"
-                     onmouseout="this.style.backgroundColor='transparent'">
-                    {tool.icon && <span style="margin-right: 0.5rem;">{tool.icon}</span>}
-                    {tool.name}
+                  <a href={tool.projectUrl || tool.url} target="_blank" rel="noopener noreferrer" class="related-tool-card">
+                    {tool.icon && <span class="tool-icon">{tool.icon}</span>}
+                    <span class="tool-name">{tool.name}</span>
                   </a>
                 ))}
               </div>
-            </details>
-          </div>
-        )}
-        
-        <a href="/" class="btn btn-secondary">
-          <svg width="16" height="16" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" style="margin-right: 0.5rem;">
-            <path d="M3 9l9-7 9 7v11a2 2 0 0 1-2 2H5a2 2 0 0 1-2-2z"/>
-            <polyline points="9,22 9,12 15,12 15,22"/>
-          </svg>
-          Zur Hauptseite
-        </a>
-      </div>
+            </div>
+          )}
+        </footer>
+      </main>
     </div>
-  </article>
+  </div>
+
+<script>
+  /** @template {Element} T
+   *  @param {string} sel
+   *  @param {Document|Element} [root=document]
+   *  @returns {T|null} */
+  const qs = (sel, root = document) => root.querySelector(sel);
+
+  /** @template {Element} T
+   *  @param {string} sel
+   *  @param {Document|Element} [root=document]
+   *  @returns {T[]} */
+  const qsa = (sel, root = document) => Array.from(root.querySelectorAll(sel));
+
+  function calculateReadingTime() {
+    /** @type {HTMLElement|null} */
+    const content = qs('.markdown-content');
+    if (!content) return;
+
+    const text = (content.textContent || content.innerText || '').trim();
+    if (!text) return;
+
+    const wordsPerMinute = 200;
+    const words = text.split(/\s+/).length;
+    const readingTime = Math.ceil(words / wordsPerMinute);
+
+    const readingTimeElement = document.getElementById('reading-time');
+    if (readingTimeElement) {
+      readingTimeElement.textContent = `~${readingTime} min`;
+    }
+  }
+
+  function generateSidebarTOC() {
+    /** @type {HTMLElement|null} */
+    const article = qs('.markdown-content');
+    /** @type {HTMLElement|null} */
+    const sidebar = qs('.article-sidebar');
+    /** @type {HTMLElement|null} */
+    const main = qs('.article-main');
+
+    if (!article || !sidebar || !main) return;
+
+    /** @type {HTMLHeadingElement[]} */
+    const headings = qsa('h1, h2, h3, h4, h5, h6', article);
+
+    if (headings.length < 2) {
+      sidebar.style.display = 'none';
+      main.style.maxWidth = '100%';
+      return;
+    }
+
+    headings.forEach((h, i) => {
+      if (!h.id) h.id = `heading-${i}`;
+    });
+
+    const tocHTML = `
+      <div class="sidebar-toc">
+        <h3 class="toc-title">Inhaltsverzeichnis</h3>
+        <nav class="toc-navigation">
+          ${headings.map((h) => {
+            const level = parseInt(h.tagName.slice(1), 10);
+            const text = (h.textContent || '').trim();
+            const id = h.id;
+            return `
+              <a href="#${id}" class="toc-item toc-level-${level}" data-heading="${id}">
+                ${text}
+              </a>
+            `;
+          }).join('')}
+        </nav>
+      </div>
+    `;
+
+    sidebar.innerHTML = tocHTML;
+
+    /** @type {HTMLAnchorElement[]} */
+    const tocItems = qsa('.toc-item', sidebar);
+
+    tocItems.forEach((item) => {
+      item.addEventListener('click', (e) => {
+        e.preventDefault();
+        const href = item.getAttribute('href');
+        if (!href || !href.startsWith('#')) return;
+
+        const target = document.getElementById(href.slice(1));
+        if (target) {
+          target.scrollIntoView({ behavior: 'smooth', block: 'start', inline: 'nearest' });
+          tocItems.forEach((i) => i.classList.remove('active'));
+          item.classList.add('active');
+        }
+      });
+    });
+
+    const updateActiveSection = () => {
+      const scrollY = window.scrollY + 100;
+      let currentId = null;
+
+      for (let i = 0; i < headings.length; i++) {
+        const h = headings[i];
+        const rect = h.getBoundingClientRect();
+        const absTop = rect.top + window.pageYOffset;
+        if (absTop <= scrollY) currentId = h.id;
+      }
+
+      if (currentId) {
+        tocItems.forEach((i) => i.classList.remove('active'));
+        /** @type {HTMLAnchorElement|null} */
+        const active = qs(`.toc-item[data-heading="${currentId}"]`, sidebar);
+        if (active) active.classList.add('active');
+      }
+    };
+
+    let ticking = false;
+    window.addEventListener('scroll', () => {
+      if (ticking) return;
+      ticking = true;
+      requestAnimationFrame(() => {
+        updateActiveSection();
+        ticking = false;
+      });
+    });
+
+    updateActiveSection();
+  }
+
+  function enhanceCodeCopy() {
+    /** @type {HTMLPreElement[]} */
+    const pres = qsa('.markdown-content pre');
+
+    pres.forEach((pre) => {
+      if (pre.dataset.copyEnhanced === 'true') return;
+      pre.dataset.copyEnhanced = 'true';
+      pre.style.position ||= 'relative';
+
+      // Try to find an existing copy button we can reuse
+      let btn =
+        pre.querySelector('.copy-btn') ||                         // our class
+        pre.querySelector('.btn-copy, .copy-button, .code-copy, .copy-code, button[aria-label*="copy" i]');
+
+      // If there is an "old" button that is NOT ours, prefer to reuse it by giving it our class.
+      if (btn && !btn.classList.contains('copy-btn')) {
+        btn.classList.add('copy-btn');
+      }
+
+      // If no button at all, create one
+      if (!btn) {
+        btn = document.createElement('button');
+        btn.type = 'button';
+        btn.className = 'copy-btn';
+        btn.setAttribute('aria-label', 'Code kopieren');
+        btn.innerHTML = `
+          <svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2"
+               aria-hidden="true"><rect x="9" y="9" width="13" height="13" rx="2"/>
+               <path d="M5 15H4a2 2 0 0 1-2-2V4a2 2 0 0 1 2-2h9a2 2 0 0 1 2 2v1"/></svg>
+          <span>Copy</span>
+        `;
+        pre.appendChild(btn);
+      }
+
+      // If there is a SECOND old button lingering (top-left in your case), hide it
+      const possibleOldButtons = pre.querySelectorAll(
+        '.btn-copy, .copy-button, .code-copy, .copy-code, button[aria-label*="copy" i]'
+      );
+      possibleOldButtons.forEach((b) => {
+        if (b !== btn) b.style.display = 'none';
+      });
+
+      // Success pill
+      if (!pre.querySelector('.copied-pill')) {
+        const pill = document.createElement('div');
+        pill.className = 'copied-pill';
+        pill.textContent = '✓ Kopiert';
+        pre.appendChild(pill);
+      }
+
+      // Screen reader live region
+      if (!pre.querySelector('.sr-live')) {
+        const live = document.createElement('div');
+        live.className = 'sr-live';
+        live.setAttribute('aria-live', 'polite');
+        Object.assign(live.style, {
+          position: 'absolute', width: '1px', height: '1px', padding: '0', margin: '-1px',
+          overflow: 'hidden', clip: 'rect(0,0,0,0)', border: '0'
+        });
+        pre.appendChild(live);
+      }
+
+      btn.addEventListener('click', async () => {
+        const code = pre.querySelector('code');
+        const text = code ? code.innerText : pre.innerText;
+        const live = pre.querySelector('.sr-live');
+
+        const copyText = async (t) => {
+          try {
+            await navigator.clipboard.writeText(t);
+            return true;
+          } catch {
+            const ta = document.createElement('textarea');
+            ta.value = t;
+            ta.style.position = 'fixed';
+            ta.style.top = '-1000px';
+            document.body.appendChild(ta);
+            ta.select();
+            const ok = document.execCommand('copy');
+            document.body.removeChild(ta);
+            return ok;
+          }
+        };
+
+        const ok = await copyText(text);
+        pre.dataset.copied = ok ? 'true' : 'false';
+        if (live) live.textContent = ok ? 'Code in die Zwischenablage kopiert' : 'Kopieren fehlgeschlagen';
+
+        window.setTimeout(() => { pre.dataset.copied = 'false'; }, 1200);
+      });
+    });
+  }
+
+  // keep your existing DOMContentLoaded; just ensure this is called
+  document.addEventListener('DOMContentLoaded', () => {
+    // existing:
+    calculateReadingTime();
+    generateSidebarTOC();
+    // new/updated:
+    enhanceCodeCopy();
+  });
+</script>
+
+
 </BaseLayout>
\ No newline at end of file
diff --git a/src/styles/auditTrail.css b/src/styles/auditTrail.css
new file mode 100644
index 0000000..d20e2f4
--- /dev/null
+++ b/src/styles/auditTrail.css
@@ -0,0 +1,407 @@
+/* src/styles/auditTrail.css - Reusable Audit Trail Styles */
+
+.audit-trail-container {
+  background-color: var(--color-bg-secondary);
+  border: 1px solid var(--color-border);
+  border-left: 4px solid var(--color-accent);
+  border-radius: 0.5rem;
+  padding: 1rem;
+  margin: 1rem 0;
+  transition: var(--transition-fast);
+}
+
+.audit-trail-header {
+  display: flex;
+  align-items: center;
+  justify-content: space-between;
+  margin-bottom: 1rem;
+}
+
+.audit-trail-header.clickable {
+  cursor: pointer;
+  padding: 0.25rem;
+  border-radius: 0.25rem;
+  margin: -0.25rem;
+  transition: var(--transition-fast);
+}
+
+.audit-trail-header.clickable:hover {
+  background-color: var(--color-bg-tertiary);
+}
+
+.audit-trail-title {
+  display: flex;
+  align-items: center;
+  gap: 1.5rem;
+  flex: 1;
+}
+
+.audit-icon {
+  display: flex;
+  align-items: center;
+  gap: 0.75rem;
+}
+
+.audit-icon-gradient {
+  width: 24px;
+  height: 24px;
+  background: linear-gradient(135deg, var(--color-accent) 0%, var(--color-primary) 100%);
+  border-radius: 50%;
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  color: white;
+  font-size: 0.75rem;
+  font-weight: bold;
+}
+
+.audit-icon h4 {
+  margin: 0;
+  font-size: 0.875rem;
+  font-weight: 600;
+  color: var(--color-accent);
+}
+
+.audit-stats {
+  display: flex;
+  gap: 1rem;
+  font-size: 0.75rem;
+  color: var(--color-text-secondary);
+}
+
+.stat-item {
+  display: flex;
+  align-items: center;
+  gap: 0.375rem;
+}
+
+.stat-dot {
+  width: 8px;
+  height: 8px;
+  border-radius: 50%;
+}
+
+.stat-time {
+  background-color: var(--color-accent);
+}
+
+.toggle-icon {
+  transition: transform var(--transition-medium);
+  color: var(--color-text-secondary);
+}
+
+.audit-trail-details {
+  display: block;
+  transition: all var(--transition-medium);
+  overflow: hidden;
+}
+
+.audit-trail-details.collapsed {
+  display: none;
+}
+
+.audit-summary {
+  background-color: var(--color-bg-tertiary);
+  padding: 1rem;
+  border-radius: 0.5rem;
+  margin-bottom: 1.5rem;
+}
+
+.summary-header {
+  font-size: 0.75rem;
+  font-weight: 600;
+  color: var(--color-accent);
+  margin-bottom: 0.75rem;
+}
+
+.summary-grid {
+  display: grid;
+  grid-template-columns: repeat(3, 1fr);
+  gap: 1rem;
+  margin-bottom: 1rem;
+}
+
+.summary-stat {
+  text-align: center;
+}
+
+.summary-value {
+  font-size: 1.125rem;
+  font-weight: 700;
+  margin-bottom: 0.25rem;
+}
+
+.summary-value.success {
+  color: var(--color-accent);
+}
+
+.summary-value.warning {
+  color: var(--color-warning);
+}
+
+.summary-label {
+  font-size: 0.6875rem;
+  color: var(--color-text-secondary);
+}
+
+.insights-section {
+  margin-top: 0.75rem;
+}
+
+.insights-header {
+  font-size: 0.6875rem;
+  font-weight: 600;
+  margin-bottom: 0.375rem;
+}
+
+.insights-header.success {
+  color: var(--color-accent);
+}
+
+.insights-header.warning {
+  color: var(--color-warning);
+}
+
+.insights-list {
+  margin: 0;
+  padding-left: 1rem;
+  font-size: 0.625rem;
+  line-height: 1.4;
+}
+
+.insights-list li {
+  margin-bottom: 0.25rem;
+}
+
+.audit-process-flow {
+  display: grid;
+  gap: 1rem;
+}
+
+.phase-group {
+  position: relative;
+}
+
+.phase-group:not(.last-phase)::after {
+  content: '';
+  position: absolute;
+  left: 13px;
+  bottom: -8px;
+  width: 2px;
+  height: 16px;
+  background: linear-gradient(to bottom, var(--color-border) 0%, transparent 100%);
+}
+
+
+.phase-icon {
+  font-size: 1rem;
+}
+
+.phase-name {
+  font-size: 0.875rem;
+  font-weight: 500;
+  color: var(--color-text);
+}
+
+.phase-divider {
+  flex: 1;
+  height: 1px;
+  background-color: var(--color-border);
+}
+
+.phase-stats {
+  display: flex;
+  align-items: center;
+  gap: 0.5rem;
+  flex-shrink: 0;
+}
+
+.confidence-bar {
+  width: 48px;
+  height: 8px;
+  background-color: var(--color-bg-tertiary);
+  border-radius: 4px;
+  overflow: hidden;
+}
+
+.confidence-fill {
+  height: 100%;
+  border-radius: 4px;
+  transition: var(--transition-fast);
+}
+
+.confidence-text {
+  font-size: 0.75rem;
+  color: var(--color-text-secondary);
+  min-width: 28px;
+}
+
+.phase-entries {
+  margin-left: 1.5rem;
+  display: grid;
+  gap: 0.5rem;
+}
+
+.audit-entry {
+  background-color: var(--color-bg);
+  border: 1px solid var(--color-border);
+  border-radius: 0.375rem;
+  padding: 0.75rem;
+  transition: var(--transition-fast);
+}
+
+.audit-entry:hover {
+  background-color: var(--color-bg-secondary);
+}
+
+.entry-main {
+  display: flex;
+  align-items: center;
+  justify-content: space-between;
+  margin-bottom: 0.5rem;
+}
+
+.entry-action {
+  font-size: 0.875rem;
+  font-weight: 500;
+  color: var(--color-text);
+}
+
+.entry-meta {
+  display: flex;
+  align-items: center;
+  gap: 0.5rem;
+  font-size: 0.75rem;
+  color: var(--color-text-secondary);
+}
+
+.confidence-indicator {
+  width: 8px;
+  height: 8px;
+  border-radius: 50%;
+}
+
+.confidence-value {
+  min-width: 28px;
+  text-align: right;
+}
+
+.processing-time {
+  min-width: 40px;
+  text-align: right;
+}
+
+.entry-details {
+  font-size: 0.75rem;
+  color: var(--color-text-secondary);
+  padding-top: 0.5rem;
+  border-top: 1px solid var(--color-border);
+}
+
+.detail-item {
+  margin-bottom: 0.25rem;
+  word-break: break-word;
+}
+
+.detail-item:last-child {
+  margin-bottom: 0;
+}
+
+.technical-toggle {
+  text-align: center;
+  margin-top: 1.5rem;
+  padding-top: 1rem;
+  border-top: 1px solid var(--color-border);
+}
+
+.technical-toggle-btn {
+  background: none;
+  border: none;
+  color: var(--color-text-secondary);
+  cursor: pointer;
+  font-size: 0.75rem;
+  padding: 0.5rem 1rem;
+  border-radius: 0.25rem;
+  transition: var(--transition-fast);
+}
+
+.technical-toggle-btn:hover {
+  background-color: var(--color-bg-secondary);
+  color: var(--color-text);
+}
+
+.technical-details {
+  margin-top: 1rem;
+  display: grid;
+  gap: 0.5rem;
+  transition: all var(--transition-medium);
+}
+
+.technical-details.collapsed {
+  display: none;
+}
+
+.technical-entry {
+  background-color: var(--color-bg-secondary);
+  border: 1px solid var(--color-border);
+  border-radius: 0.375rem;
+  padding: 0.75rem;
+  font-size: 0.75rem;
+}
+
+.technical-header {
+  display: flex;
+  justify-content: space-between;
+  align-items: center;
+  margin-bottom: 0.5rem;
+  font-family: 'SF Mono', 'Monaco', 'Menlo', 'Consolas', monospace;
+}
+
+.technical-phase {
+  font-weight: 600;
+  color: var(--color-primary);
+}
+
+.technical-time {
+  color: var(--color-text-secondary);
+}
+
+.technical-content {
+  display: grid;
+  gap: 0.375rem;
+}
+
+.technical-row {
+  color: var(--color-text-secondary);
+  word-break: break-word;
+}
+
+/* Responsive Design */
+@media (width <= 768px) {
+  .audit-stats {
+    flex-direction: column;
+    gap: 0.5rem;
+    align-items: flex-start;
+  }
+
+  .summary-grid {
+    grid-template-columns: 1fr;
+    gap: 0.75rem;
+  }
+
+  .phase-divider {
+    display: none;
+  }
+
+  .entry-main {
+    flex-direction: column;
+    align-items: flex-start;
+    gap: 0.5rem;
+  }
+
+  .technical-header {
+    flex-direction: column;
+    align-items: flex-start;
+    gap: 0.25rem;
+  }
+}
\ No newline at end of file
diff --git a/src/styles/autocomplete.css b/src/styles/autocomplete.css
new file mode 100644
index 0000000..3f73a03
--- /dev/null
+++ b/src/styles/autocomplete.css
@@ -0,0 +1,121 @@
+/* ============================================================================
+   AUTOCOMPLETE COMPONENT STYLES
+   ============================================================================ */
+
+.autocomplete-dropdown {
+  position: absolute;
+  top: 100%;
+  left: 0;
+  right: 0;
+  background: var(--color-bg);
+  border: 1px solid var(--color-border);
+  border-radius: 0.375rem;
+  box-shadow: var(--shadow-lg);
+  max-height: 200px;
+  overflow-y: auto;
+  z-index: 1000;
+  display: none;
+}
+
+.autocomplete-option {
+  padding: 0.5rem;
+  cursor: pointer;
+  border-bottom: 1px solid var(--color-border-light);
+  transition: background-color 0.15s ease;
+}
+
+.autocomplete-option:last-child {
+  border-bottom: none;
+}
+
+.autocomplete-option:hover {
+  background-color: var(--color-bg-secondary);
+}
+
+.autocomplete-item {
+  font-size: 0.875rem;
+  color: var(--color-text);
+}
+
+.autocomplete-no-results {
+  padding: 0.75rem;
+  text-align: center;
+  color: var(--color-text-secondary);
+  font-size: 0.875rem;
+  font-style: italic;
+}
+
+.autocomplete-selected {
+  display: flex;
+  flex-wrap: wrap;
+  gap: 0.5rem;
+  margin-bottom: 0.5rem;
+  min-height: 1.5rem;
+}
+
+.autocomplete-tag {
+  background-color: var(--color-primary);
+  color: white;
+  padding: 0.25rem 0.5rem;
+  border-radius: 0.25rem;
+  font-size: 0.875rem;
+  display: flex;
+  align-items: center;
+  gap: 0.25rem;
+  animation: fadeInScale 0.2s ease-out;
+}
+
+.autocomplete-remove {
+  background: none;
+  border: none;
+  color: white;
+  font-weight: bold;
+  cursor: pointer;
+  padding: 0;
+  width: 1rem;
+  height: 1rem;
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  border-radius: 50%;
+  transition: background-color 0.15s ease;
+}
+
+.autocomplete-remove:hover {
+  background-color: rgba(255, 255, 255, 0.2);
+}
+
+/* Animation for tag appearance */
+@keyframes fadeInScale {
+  from {
+    opacity: 0;
+    transform: scale(0.8);
+  }
+  to {
+    opacity: 1;
+    transform: scale(1);
+  }
+}
+
+/* Ensure autocomplete container has relative positioning */
+.autocomplete-container {
+  position: relative;
+}
+
+/* Custom scrollbar for autocomplete dropdown */
+.autocomplete-dropdown::-webkit-scrollbar {
+  width: 6px;
+}
+
+.autocomplete-dropdown::-webkit-scrollbar-track {
+  background: var(--color-bg-secondary);
+}
+
+.autocomplete-dropdown::-webkit-scrollbar-thumb {
+  background: var(--color-border);
+  border-radius: 3px;
+}
+
+.autocomplete-dropdown::-webkit-scrollbar-thumb:hover {
+  background: var(--color-text-secondary);
+}
\ No newline at end of file
diff --git a/src/styles/global.css b/src/styles/global.css
index be43144..523eaa1 100644
--- a/src/styles/global.css
+++ b/src/styles/global.css
@@ -14,72 +14,7 @@
   padding: 0;
 }
 
-/* ===================================================================
-   2. CSS VARIABLES AND THEMING
-   ================================================================= */
 
-:root {
-  /* Light Theme Colors */
-  --color-bg: #fff;
-  --color-bg-secondary: #f8fafc;
-  --color-bg-tertiary: #e2e8f0;
-  --color-text: #1e293b;
-  --color-text-secondary: #64748b;
-  --color-border: #cbd5e1;
-  --color-primary: #2563eb;
-  --color-primary-hover: #1d4ed8;
-  --color-accent: #059669;
-  --color-accent-hover: #047857;
-  --color-warning: #d97706;
-  --color-error: #dc2626;
-  
-  /* Enhanced card type colors */
-  --color-hosted: #7c3aed;
-  --color-hosted-bg: #f3f0ff;
-  --color-oss: #059669;
-  --color-oss-bg: #ecfdf5;
-  --color-method: #0891b2;
-  --color-method-bg: #f0f9ff;
-  --color-concept: #ea580c;
-  --color-concept-bg: #fff7ed;
-  
-  /* Shadows */
-  --shadow-sm: 0 1px 2px 0 rgb(0 0 0 / 5%);
-  --shadow-md: 0 4px 6px -1px rgb(0 0 0 / 10%);
-  --shadow-lg: 0 10px 15px -3px rgb(0 0 0 / 10%);
-  
-  /* Transitions */
-  --transition-fast: all 0.2s ease;
-  --transition-medium: all 0.3s ease;
-}
-
-[data-theme="dark"] {
-  --color-bg: #0f172a;
-  --color-bg-secondary: #1e293b;
-  --color-bg-tertiary: #334155;
-  --color-text: #f1f5f9;
-  --color-text-secondary: #94a3b8;
-  --color-border: #475569;
-  --color-primary: #3b82f6;
-  --color-primary-hover: #60a5fa;
-  --color-accent: #10b981;
-  --color-accent-hover: #34d399;
-  --color-warning: #f59e0b;
-  --color-error: #f87171;
-  
-  --color-hosted: #a855f7;
-  --color-hosted-bg: #2e1065;
-  --color-oss: #10b981;
-  --color-oss-bg: #064e3b;
-  --color-method: #0891b2;
-  --color-method-bg: #164e63;
-  --color-concept: #f97316;
-  --color-concept-bg: #7c2d12;
-  
-  --shadow-sm: 0 1px 2px 0 rgb(0 0 0 / 30%);
-  --shadow-md: 0 4px 6px -1px rgb(0 0 0 / 40%);
-  --shadow-lg: 0 10px 15px -3px rgb(0 0 0 / 50%);
-}
 
 /* ===================================================================
    3. BASE HTML ELEMENTS
@@ -747,11 +682,13 @@ input[type="checkbox"] {
    ================================================================= */
 
 .tool-card {
-  height: 300px;
+  min-height: 300px;
+  max-height: 350px;
   display: flex;
   flex-direction: column;
   padding: 1.25rem;
   cursor: pointer;
+  overflow: hidden;
 }
 
 .tool-card-header {
@@ -760,6 +697,7 @@ input[type="checkbox"] {
   align-items: flex-start;
   min-height: 2.5rem;
   margin-bottom: 0.75rem;
+  flex-shrink: 0;
 }
 
 .tool-card-header h3 {
@@ -788,6 +726,7 @@ input[type="checkbox"] {
   font-size: 0.875rem;
   margin-bottom: 0.5rem;
   word-break: break-word;
+  flex-shrink: 0;
 }
 
 .tool-card-metadata {
@@ -796,6 +735,7 @@ input[type="checkbox"] {
   gap: 1rem;
   margin-bottom: 0.75rem;
   line-height: 1;
+  flex-shrink: 0;
 }
 
 .metadata-item {
@@ -824,10 +764,11 @@ input[type="checkbox"] {
   flex-wrap: wrap;
   gap: 0.25rem;
   max-height: 3.5rem;
+  min-height: 0;
   overflow: hidden;
   position: relative;
   margin-bottom: 1rem;
-  flex-shrink: 0;
+  flex: 0 0 3.5rem;
 }
 
 .tool-tags-container::after {
@@ -861,6 +802,8 @@ input[type="checkbox"] {
 .tool-card-buttons {
   margin-top: auto;
   flex-shrink: 0;
+  flex-basis: auto;
+  min-height: 0;
 }
 
 .button-row {
@@ -1689,6 +1632,156 @@ input[type="checkbox"] {
   font-size: 0.75rem;
 }
 
+/* ===================================================================
+   SEMANTIC SEARCH STYLES - INLINE VERSION (REPLACE EXISTING)
+   ================================================================= */
+
+/* Search row with inline semantic toggle */
+.search-row {
+  display: flex;
+  align-items: center;
+  gap: 1rem;
+}
+
+.search-wrapper {
+  flex: 1;
+  position: relative;
+  display: flex;
+  align-items: center;
+}
+
+/* Inline semantic search toggle */
+.semantic-search-inline {
+  flex-shrink: 0;
+}
+
+.semantic-toggle-wrapper {
+  display: flex;
+  align-items: center;
+  gap: 0.5rem;
+  cursor: pointer;
+  padding: 0.5rem 0.75rem;
+  border-radius: 0.375rem;
+  border: 1px solid var(--color-border);
+  background-color: var(--color-bg-secondary);
+  transition: var(--transition-fast);
+  user-select: none;
+  white-space: nowrap;
+}
+
+.semantic-toggle-wrapper:hover {
+  background-color: var(--color-bg-tertiary);
+  border-color: var(--color-accent);
+}
+
+.semantic-toggle-wrapper input[type="checkbox"] {
+  display: none;
+}
+
+.semantic-checkbox-custom {
+  width: 16px;
+  height: 16px;
+  border: 2px solid var(--color-border);
+  border-radius: 0.25rem;
+  background-color: var(--color-bg);
+  transition: var(--transition-fast);
+  position: relative;
+  flex-shrink: 0;
+}
+
+.semantic-checkbox-custom::after {
+  content: '';
+  position: absolute;
+  top: 50%;
+  left: 50%;
+  transform: translate(-50%, -50%) scale(0);
+  width: 8px;
+  height: 8px;
+  background-color: white;
+  border-radius: 0.125rem;
+  transition: var(--transition-fast);
+}
+
+.semantic-toggle-wrapper input:checked + .semantic-checkbox-custom {
+  background-color: var(--color-accent);
+  border-color: var(--color-accent);
+}
+
+.semantic-toggle-wrapper input:checked + .semantic-checkbox-custom::after {
+  transform: translate(-50%, -50%) scale(1);
+}
+
+.semantic-toggle-label {
+  display: flex;
+  align-items: center;
+  gap: 0.375rem;
+  font-size: 0.8125rem;
+  font-weight: 500;
+  color: var(--color-text);
+}
+
+.semantic-toggle-label svg {
+  width: 14px;
+  height: 14px;
+  color: var(--color-accent);
+  flex-shrink: 0;
+}
+
+/* Semantic Status Display */
+.semantic-status {
+  margin-top: 0.75rem;
+  padding: 0.375rem 0.75rem;
+  background-color: var(--color-accent);
+  color: white;
+  border-radius: 1rem;
+  font-size: 0.75rem;
+  font-weight: 500;
+  text-align: center;
+  opacity: 0.9;
+}
+
+.semantic-results-count {
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  gap: 0.375rem;
+}
+
+.semantic-results-count::before {
+  content: '🧠';
+  font-size: 0.875rem;
+}
+
+/* Responsive adjustments */
+@media (width <= 768px) {
+  .search-row {
+    flex-direction: column;
+    align-items: stretch;
+    gap: 0.75rem;
+  }
+  
+  .semantic-toggle-wrapper {
+    justify-content: center;
+    padding: 0.625rem;
+  }
+  
+  .semantic-toggle-label {
+    font-size: 0.875rem;
+  }
+}
+
+@media (width <= 480px) {
+  .semantic-toggle-label span {
+    display: none; /* Hide "Semantisch" text on very small screens */
+  }
+  
+  .semantic-toggle-wrapper {
+    padding: 0.5rem;
+    min-width: 40px;
+    justify-content: center;
+  }
+}
+
 /* ===================================================================
    16. AI INTERFACE (CONSOLIDATED)
    ================================================================= */
@@ -1970,9 +2063,12 @@ input[type="checkbox"] {
   margin: 0 auto 0.75rem;
 }
 
-.phase-info { 
-  flex: 1; 
-  min-width: 0; 
+.phase-info {
+  display: flex;
+  flex-direction: column;
+  gap: 0.75rem;
+  flex: 1;
+  min-width: 0;
 }
 
 .phase-title {
@@ -3434,6 +3530,16 @@ footer {
     width: 14px;
     height: 14px;
   }
+
+  .tool-card {
+    min-height: 280px;
+    max-height: 320px;
+  }
+  
+  .tool-tags-container {
+    max-height: 3rem;
+    flex: 0 0 3rem;
+  }
 }
 
 @media (width <= 640px) {
@@ -3593,179 +3699,30 @@ footer {
   }
 }
 
-
-/* ===================================================================
-   MIGRATION UTILITIES - Additional classes for inline style migration
-   ================================================================= */
-
-/* Alignment utilities */
 .align-middle { vertical-align: middle; }
 
-/* Font style utilities */
 .italic { font-style: italic; }
 
-/* Border width utilities */
 .border-2 { border-width: 2px; }
 
-/* Border color utilities */
 .border-accent { border-color: var(--color-accent); }
 
-/* Card variants for complex backgrounds */
 .card-warning {
   background: linear-gradient(135deg, var(--color-warning) 0%, var(--color-accent) 100%);
   color: white;
 }
 
-/* Header variants for complex gradient combinations */
 .header-primary {
   background: linear-gradient(135deg, var(--color-primary) 0%, var(--color-accent) 100%);
   color: white;
 }
 
-/* Ensure we have all text size variants */
 .text-2xl { font-size: 1.5rem; }
 
-/* Additional spacing utilities that might be missing */
 .py-8 { padding-top: 2rem; padding-bottom: 2rem; }
 .px-4 { padding-left: 1rem; padding-right: 1rem; }
 .my-6 { margin-top: 1.5rem; margin-bottom: 1.5rem; }
 
-/* Flex utilities that might be missing */
 .flex-1 { flex: 1; }
 
-/* Additional rounded variants */
-.rounded-xl { border-radius: 0.75rem; }
-
-/* ===================================================================
-   23. MARKDOWN CONTENT
-   ================================================================= */
-
-.markdown-content h1,
-.markdown-content h2,
-.markdown-content h3,
-.markdown-content h4,
-.markdown-content h5,
-.markdown-content h6 {
-  margin-top: 2rem;
-  margin-bottom: 1rem;
-}
-
-.markdown-content h1:first-child,
-.markdown-content h2:first-child,
-.markdown-content h3:first-child {
-  margin-top: 0;
-}
-
-.markdown-content p {
-  margin-bottom: 1rem;
-}
-
-.markdown-content ul,
-.markdown-content ol {
-  margin-bottom: 1rem;
-  padding-left: 1.5rem;
-}
-
-.markdown-content li {
-  margin-bottom: 0.5rem;
-}
-
-.markdown-content pre {
-  background-color: var(--color-bg-tertiary);
-  border: 1px solid var(--color-border);
-  border-radius: 0.5rem;
-  padding: 1rem;
-  margin: 1.5rem 0;
-  overflow-x: auto;
-  font-size: 0.875rem;
-}
-
-.markdown-content code:not(pre code) {
-  background-color: var(--color-bg-secondary);
-  border: 1px solid var(--color-border);
-  border-radius: 0.25rem;
-  padding: 0.125rem 0.375rem;
-  font-size: 0.875rem;
-}
-
-.markdown-content table {
-  width: 100%;
-  border-collapse: collapse;
-  margin: 1.5rem 0;
-  background-color: var(--color-bg);
-  border-radius: 0.5rem;
-  overflow: hidden;
-  border: 1px solid var(--color-border);
-}
-
-.markdown-content th,
-.markdown-content td {
-  padding: 0.75rem;
-  text-align: left;
-  border-bottom: 1px solid var(--color-border);
-}
-
-.markdown-content th {
-  background-color: var(--color-bg-secondary);
-  font-weight: 600;
-}
-
-.markdown-content blockquote {
-  border-left: 4px solid var(--color-primary);
-  background-color: var(--color-bg-secondary);
-  padding: 1rem 1.5rem;
-  margin: 1.5rem 0;
-  border-radius: 0 0.5rem 0.5rem 0;
-}
-
-.markdown-content hr {
-  border: none;
-  border-top: 1px solid var(--color-border);
-  margin: 2rem 0;
-}
-
-/* ===================================================================
-   26. ENHANCED AUDIT TRAIL STYLES
-   ================================================================= */
-
-.audit-process-flow {
-  position: relative;
-}
-
-.phase-group {
-  position: relative;
-}
-
-.phase-group:not(:last-child)::after {
-  content: '';
-  position: absolute;
-  left: 13px;
-  bottom: -8px;
-  width: 2px;
-  height: 16px;
-  background: linear-gradient(to bottom, var(--color-border) 0%, transparent 100%);
-}
-
-.toggle-icon {
-  transition: transform 0.2s ease;
-}
-
-/* Hover effects for audit entries */
-.audit-trail-details .hover\\:bg-secondary:hover {
-  background-color: var(--color-bg-secondary);
-}
-
-/* Responsive adjustments for audit trail */
-@media (width <= 768px) {
-  .audit-process-flow .grid-cols-3 {
-    grid-template-columns: 1fr;
-    gap: 1rem;
-  }
-  
-  .phase-group .flex {
-    flex-direction: column;
-    align-items: flex-start;
-    gap: 0.5rem;
-  }
-  
-}
\ No newline at end of file
+.rounded-xl { border-radius: 0.75rem; }
\ No newline at end of file
diff --git a/src/styles/knowledgebase.css b/src/styles/knowledgebase.css
new file mode 100644
index 0000000..0757479
--- /dev/null
+++ b/src/styles/knowledgebase.css
@@ -0,0 +1,661 @@
+/* ==========================================================================
+   0) FOUNDATIONS
+   - Typography, rhythm, utility tokens, base elements
+   ========================================================================== */
+
+:root {
+  /* Optional: gentle defaults if not already defined upstream */
+  --radius-xs: 0.25rem;
+  --radius-sm: 0.375rem;
+  --radius-md: 0.5rem;
+  --radius-lg: 0.75rem;
+  --radius-xl: 1rem;
+
+  --shadow-xs: 0 1px 2px rgba(0,0,0,0.06);
+  --shadow-sm: 0 2px 8px rgba(0,0,0,0.06);
+  --shadow-md: 0 6px 18px rgba(0,0,0,0.10);
+  --shadow-lg: 0 12px 30px rgba(0,0,0,0.16);
+
+  --ease-quick: 0.2s ease;
+}
+
+/* Base text container for articles */
+:where(.markdown-content) {
+  max-width: 70ch;
+  margin: 0 auto;
+  font-size: 1.125rem;
+  line-height: 1.8;
+  color: var(--color-text);
+}
+
+:where(.markdown-content) * {
+  scroll-margin-top: 100px;
+}
+
+/* ==========================================================================
+   1) HEADINGS & TEXT
+   ========================================================================== */
+
+:where(.markdown-content) h1,
+:where(.markdown-content) h2,
+:where(.markdown-content) h3,
+:where(.markdown-content) h4,
+:where(.markdown-content) h5,
+:where(.markdown-content) h6 {
+  font-weight: 700;
+  line-height: 1.25;
+  color: var(--color-text);
+  margin: 0;
+}
+
+:where(.markdown-content) h1 {
+  font-size: 2.5rem;
+  margin: 3rem 0 1.5rem 0;
+  border-bottom: 3px solid var(--color-primary);
+  padding-bottom: 0.5rem;
+}
+
+:where(.markdown-content) h2 {
+  font-size: 2rem;
+  margin: 2.5rem 0 1rem 0;
+  border-bottom: 2px solid var(--color-border);
+  padding-bottom: 0.375rem;
+}
+
+:where(.markdown-content) h3 {
+  font-size: 1.5rem;
+  margin: 2rem 0 0.75rem 0;
+  color: var(--color-primary);
+}
+
+:where(.markdown-content) h4 {
+  font-size: 1.25rem;
+  margin: 1.5rem 0 0.5rem 0;
+}
+
+:where(.markdown-content) h5,
+:where(.markdown-content) h6 {
+  font-size: 1.125rem;
+  margin: 1rem 0 0.5rem 0;
+  font-weight: 600;
+}
+
+:where(.markdown-content) p {
+  margin: 0 0 1.5rem 0;
+  text-align: justify;
+  hyphens: auto;
+}
+
+:where(.markdown-content) strong { font-weight: 700; color: var(--color-text); }
+:where(.markdown-content) em { font-style: italic; color: var(--color-text-secondary); }
+
+/* Links with refined hover state */
+:where(.markdown-content) a {
+  color: var(--color-primary);
+  text-decoration: underline;
+  text-decoration-color: transparent;
+  transition: var(--ease-quick);
+}
+:where(.markdown-content) a:hover {
+  text-decoration-color: var(--color-primary);
+  background-color: var(--color-bg-secondary);
+  padding: 0.125rem 0.25rem;
+  border-radius: var(--radius-xs);
+}
+/* External link indicator */
+:where(.markdown-content) a[href^="http"]:not([href*="localhost"]):not([href*="127.0.0.1"])::after {
+  content: " ↗";
+  font-size: 0.875rem;
+  color: var(--color-text-secondary);
+}
+
+/* ==========================================================================
+   2) LISTS, DEFINITIONS, TABLES, QUOTES
+   ========================================================================== */
+
+:where(.markdown-content) ul,
+:where(.markdown-content) ol {
+  margin: 1.5rem 0;
+  padding-left: 2rem;
+}
+
+:where(.markdown-content) li {
+  margin-bottom: 0.75rem;
+  line-height: 1.2;
+}
+:where(.markdown-content) li > ul,
+:where(.markdown-content) li > ol {
+  margin: 0.5rem 0;
+}
+
+:where(.markdown-content) dl { margin: 1.5rem 0; }
+:where(.markdown-content) dt {
+  font-weight: 700;
+  margin: 1rem 0 0.5rem 0;
+  color: var(--color-primary);
+}
+:where(.markdown-content) dd {
+  margin-left: 1.5rem;
+  margin-bottom: 0.75rem;
+  padding-left: 1rem;
+  border-left: 2px solid var(--color-border);
+}
+
+:where(.markdown-content) table {
+  width: 100%;
+  border-collapse: collapse;
+  margin: 1.5rem 0;
+  background-color: var(--color-bg);
+  border-radius: var(--radius-md);
+  overflow: hidden;
+  border: 1px solid var(--color-border);
+}
+:where(.markdown-content) th,
+:where(.markdown-content) td {
+  padding: 0.75rem;
+  text-align: left;
+  border-bottom: 1px solid var(--color-border);
+}
+:where(.markdown-content) th {
+  background-color: var(--color-bg-secondary);
+  font-weight: 600;
+}
+
+:where(.markdown-content) blockquote {
+  border-left: 4px solid var(--color-primary);
+  background-color: var(--color-bg-secondary);
+  padding: 1rem 1.5rem;
+  margin: 1.5rem 0;
+  border-radius: 0 var(--radius-md) var(--radius-md) 0;
+}
+
+:where(.markdown-content) hr {
+  border: none;
+  border-top: 1px solid var(--color-border);
+  margin: 2rem 0;
+}
+
+/* ==========================================================================
+   3) CODE & INLINE CODE
+   ========================================================================== */
+
+:where(.markdown-content) pre {
+  background-color: var(--color-bg-tertiary);
+  border: 1px solid var(--color-border);
+  border-radius: 0.75rem;
+  padding: 1.5rem;
+  margin: 2rem 0;
+  overflow-x: auto;
+  font-size: 0.875rem;
+  line-height: 1.6;
+  position: relative;
+  box-shadow: var(--shadow-sm);
+  padding-left: 1.5rem;
+}
+
+:where(.markdown-content) pre::before {
+  content: attr(data-language);
+  position: absolute;
+  top: 0.5rem;
+  left: 0.75rem;
+  right: auto;
+  z-index: 1;
+  display: inline-block;
+  padding: 0.15rem 0.5rem;
+  border-radius: var(--radius-sm);
+  background: var(--color-bg);
+  border: 1px solid var(--color-border);
+  color: var(--color-text-secondary);
+  text-transform: uppercase;
+  font-weight: 600;
+  letter-spacing: 0.05em;
+  font-size: 0.75rem;
+}
+
+:where(.markdown-content) pre[data-language]::before {
+  top: 0.5rem;
+  left: 0.75rem;
+  position: absolute;
+  z-index: 1;
+  display: inline-block;
+  padding: 0.15rem 0.5rem;
+  border-radius: var(--radius-sm);
+  background: var(--color-bg);
+  border: 1px solid var(--color-border);
+  color: var(--color-text-secondary);
+  line-height: 1;
+}
+
+:where(.markdown-content) code:not(pre code) {
+  background-color: var(--color-bg-secondary);
+  border: 1px solid var(--color-border);
+  border-radius: var(--radius-sm);
+  padding: 0.25rem 0.5rem;
+  font-size: 0.875rem;
+  font-family: 'SF Mono','Monaco','Menlo','Consolas',monospace;
+  color: var(--color-text);
+}
+
+/* Language badges (keep if your renderer sets data-language) */
+:where(.markdown-content) pre[data-language="bash"]::before { content: "BASH"; color: #4EAA25; }
+:where(.markdown-content) pre[data-language="javascript"]::before { content: "JS"; color: #F7DF1E; }
+:where(.markdown-content) pre[data-language="python"]::before { content: "PYTHON"; color: #3776AB; }
+:where(.markdown-content) pre[data-language="sql"]::before { content: "SQL"; color: #336791; }
+:where(.markdown-content) pre[data-language="yaml"]::before { content: "YAML"; color: #CB171E; }
+:where(.markdown-content) pre[data-language="json"]::before { content: "JSON"; color: #000000; }
+
+/* Keyboard UI hints */
+:where(.markdown-content) kbd {
+  background-color: var(--color-bg-tertiary);
+  border: 1px solid var(--color-border);
+  border-radius: var(--radius-xs);
+  padding: 0.125rem 0.375rem;
+  font-size: 0.75rem;
+  font-family: monospace;
+  box-shadow: var(--shadow-xs);
+  margin: 0 0.125rem;
+}
+
+:where(.markdown-content) .copy-btn {
+  position: absolute;
+  top: 0.5rem; right: 0.5rem;
+  display: inline-flex; align-items: center; gap: 0.4rem;
+  padding: 0.35rem 0.6rem;
+  border: 1px solid var(--color-border);
+  background: var(--color-bg);
+  border-radius: var(--radius-sm);
+  font-size: 0.75rem;
+  cursor: pointer;
+  box-shadow: var(--shadow-sm);
+  line-height: 1;
+  z-index: 2;
+  transition: var(--transition-fast);
+  color: var(--color-text);
+}
+
+:where(.markdown-content) .copy-btn:hover {
+  background: var(--color-bg-secondary);
+}
+:where(.markdown-content) .copy-btn:focus-visible {
+  outline: 2px solid color-mix(in srgb, var(--color-primary) 60%, transparent);
+  outline-offset: 2px;
+}
+
+:where(.markdown-content) .copy-btn svg {
+  width: 14px; height: 14px;
+}
+
+[data-theme="dark"] :where(.markdown-content) .copy-btn {
+  background: var(--color-bg-secondary);
+  border-color: color-mix(in srgb, var(--color-border) 80%, transparent);
+  box-shadow: 0 0 0 1px color-mix(in srgb, var(--color-bg-tertiary) 40%, transparent), var(--shadow-sm);
+}
+
+:where(.markdown-content) pre .copied-pill {
+  position: absolute;
+  top: 0.5rem; right: 0.5rem;
+  padding: 0.35rem 0.6rem;
+  border-radius: var(--radius-sm);
+  font-size: 0.75rem;
+  font-weight: 600;
+  background: var(--color-primary);
+  color: #fff;
+  opacity: 0; transform: translateY(-6px) scale(0.98);
+  pointer-events: none;
+  z-index: 2;
+}
+
+
+/* Success state driven by [data-copied="true"] on <pre> */
+:where(.markdown-content) pre[data-copied="true"] {
+  box-shadow: 0 0 0 2px color-mix(in srgb, var(--color-primary) 30%, transparent) inset, var(--shadow-sm);
+}
+:where(.markdown-content) pre[data-copied="true"] .copy-btn { opacity: 0; }
+:where(.markdown-content) pre[data-copied="true"] .copied-pill {
+  opacity: 1; transform: translateY(0) scale(1);
+}
+
+/* Smooth but gentle motion */
+@media (prefers-reduced-motion: no-preference) {
+  :where(.markdown-content) pre,
+  :where(.markdown-content) .copy-btn,
+  :where(.markdown-content) pre .copied-pill {
+    transition: 180ms ease;
+  }
+}
+
+
+
+/* ==========================================================================
+   4) CALLOUTS, STEPS, HIGHLIGHT BOX
+   ========================================================================== */
+
+:where(.markdown-content) .callout {
+  border-left: 4px solid;
+  border-radius: 0.5rem;
+  margin: 2rem 0;
+  padding: 1.25rem 1.5rem;
+  background-color: var(--color-bg-secondary);
+  border-color: var(--color-border);
+  box-shadow: var(--shadow-sm);
+}
+
+:where(.markdown-content) .callout-header {
+  display: flex;
+  align-items: center;
+  gap: 0.75rem;
+  margin-bottom: 0.75rem;
+  font-weight: 600;
+  font-size: 0.9375rem;
+}
+
+:where(.markdown-content) .callout-icon {
+  width: 1.25rem;
+  height: 1.25rem;
+  flex-shrink: 0;
+}
+
+/* Callout variants */
+:where(.markdown-content) .callout-note {
+  border-left-color: var(--color-primary);
+  background-color: color-mix(in srgb, var(--color-primary) 8%, var(--color-bg-secondary));
+}
+:where(.markdown-content) .callout-note .callout-header { color: var(--color-primary); }
+
+:where(.markdown-content) .callout-tip {
+  border-left-color: var(--color-accent);
+  background-color: color-mix(in srgb, var(--color-accent) 8%, var(--color-bg-secondary));
+}
+:where(.markdown-content) .callout-tip .callout-header { color: var(--color-accent); }
+
+:where(.markdown-content) .callout-warning {
+  border-left-color: var(--color-warning);
+  background-color: color-mix(in srgb, var(--color-warning) 8%, var(--color-bg-secondary));
+}
+:where(.markdown-content) .callout-warning .callout-header { color: var(--color-warning); }
+
+:where(.markdown-content) .callout-danger {
+  border-left-color: var(--color-error);
+  background-color: color-mix(in srgb, var(--color-error) 8%, var(--color-bg-secondary));
+}
+:where(.markdown-content) .callout-danger .callout-header { color: var(--color-error); }
+
+/* Highlight feature box */
+:where(.markdown-content) .highlight-box {
+  background: linear-gradient(135deg, var(--color-accent) 0%, var(--color-primary) 100%);
+  color: white;
+  padding: 2rem;
+  border-radius: 1rem;
+  margin: 2rem 0;
+  text-align: center;
+  box-shadow: var(--shadow-md);
+}
+:where(.markdown-content) .highlight-box h3 { margin: 0; color: white; }
+
+/* Steps */
+:where(.markdown-content) .steps { counter-reset: step-counter; margin: 2rem 0; }
+:where(.markdown-content) .step {
+  counter-increment: step-counter;
+  margin: 1.25rem 0;
+  padding: 1.25rem 1.5rem;
+  background-color: var(--color-bg-secondary);
+  border-radius: 0.75rem;
+  border-left: 4px solid var(--color-primary);
+  position: relative;
+}
+:where(.markdown-content) .step::before {
+  content: counter(step-counter);
+  position: absolute;
+  left: -0.75rem;
+  top: 1rem;
+  background-color: var(--color-primary);
+  color: white;
+  width: 1.5rem;
+  height: 1.5rem;
+  border-radius: 50%;
+  display: grid;
+  place-items: center;
+  font-weight: 700;
+  font-size: 0.875rem;
+}
+:where(.markdown-content) .step h4 { margin-top: 0; color: var(--color-primary); }
+
+/* ==========================================================================
+   5) ARTICLE LAYOUT (Header, Meta, Sidebar, Footer)
+   ========================================================================== */
+
+.article-layout {
+  max-width: 1400px;
+  margin: 0 auto;
+  padding: 0 1rem;
+}
+
+/* Header */
+.article-header {
+  background: linear-gradient(135deg, var(--color-bg-secondary) 0%, var(--color-bg-tertiary) 100%);
+  border: 1px solid var(--color-border);
+  border-radius: 1rem;
+  margin-bottom: 2rem;
+  overflow: hidden;
+}
+.article-header-content { padding: 2rem; }
+
+.breadcrumb {
+  display: flex;
+  align-items: center;
+  gap: 0.5rem;
+  margin-bottom: 1rem;
+  font-size: 0.875rem;
+}
+.breadcrumb-link {
+  color: var(--color-primary);
+  text-decoration: none;
+  transition: var(--ease-quick);
+}
+.breadcrumb-link:hover { text-decoration: underline; }
+.breadcrumb-separator { color: var(--color-text-secondary); }
+.breadcrumb-current { color: var(--color-text-secondary); font-weight: 500; }
+
+.article-tags {
+  display: flex;
+  flex-wrap: wrap;
+  gap: 0.5rem;
+  margin-bottom: 1.5rem;
+}
+.article-tag {
+  padding: 0.25rem 0.75rem;
+  background-color: var(--color-bg);
+  border: 1px solid var(--color-border);
+  border-radius: 1rem;
+  font-size: 0.75rem;
+  font-weight: 500;
+  color: var(--color-text-secondary);
+}
+.article-tag-category {
+  background-color: var(--color-primary);
+  color: white;
+  border-color: var(--color-primary);
+}
+
+.article-title-section { margin-bottom: 2rem; }
+.article-title {
+  font-size: 2.5rem;
+  font-weight: 700;
+  margin: 0 0 1rem 0;
+  color: var(--color-primary);
+  line-height: 1.2;
+}
+.article-icon { margin-right: 1rem; font-size: 2rem; }
+.article-description { font-size: 1.25rem; color: var(--color-text-secondary); margin: 0; line-height: 1.5; }
+
+.article-metadata-grid {
+  display: grid;
+  grid-template-columns: repeat(auto-fit, minmax(200px, 1fr));
+  gap: 1.5rem;
+  margin-bottom: 2rem;
+  padding: 1.5rem;
+  background-color: var(--color-bg);
+  border-radius: 0.75rem;
+  border: 1px solid var(--color-border);
+}
+.metadata-item { display: flex; flex-direction: column; gap: 0.5rem; }
+.metadata-label {
+  font-size: 0.75rem; font-weight: 600; text-transform: uppercase;
+  letter-spacing: 0.05em; color: var(--color-text-secondary);
+}
+.metadata-value { font-size: 0.9375rem; font-weight: 500; color: var(--color-text); }
+.metadata-badges { display: flex; flex-wrap: wrap; gap: 0.5rem; }
+
+.article-actions { display: flex; gap: 1rem; justify-content: center; }
+
+/* Content wrapper with sidebar */
+.article-content-wrapper {
+  display: grid;
+  grid-template-columns: 280px 1fr;
+  gap: 3rem;
+  align-items: start;
+}
+.article-sidebar {
+  position: sticky;
+  top: 6rem;
+  max-height: calc(100vh - 8rem);
+  overflow-y: auto;
+}
+
+/* Sidebar TOC */
+.sidebar-toc {
+  background-color: var(--color-bg-secondary);
+  border: 1px solid var(--color-border);
+  border-radius: 0.75rem;
+  padding: 1.5rem;
+}
+.toc-title {
+  margin: 0 0 1rem 0;
+  font-size: 1rem;
+  font-weight: 600;
+  color: var(--color-primary);
+}
+.toc-navigation { display: flex; flex-direction: column; gap: 0.25rem; }
+.toc-item {
+  display: block;
+  padding: 0.5rem 0.75rem;
+  color: var(--color-text);
+  text-decoration: none;
+  border-radius: var(--radius-sm);
+  font-size: 0.875rem;
+  line-height: 1.4;
+  transition: var(--ease-quick);
+  border-left: 3px solid transparent;
+}
+.toc-item:hover {
+  background-color: var(--color-bg-tertiary);
+  color: var(--color-primary);
+  border-left-color: var(--color-primary);
+}
+.toc-item.active {
+  background-color: var(--color-primary);
+  color: white;
+  border-left-color: var(--color-accent);
+}
+
+/* Indentation by level (JS sets .toc-level-X) */
+.toc-level-1 { padding-left: 0.75rem; font-weight: 600; }
+.toc-level-2 { padding-left: 1rem; font-weight: 500; }
+.toc-level-3 { padding-left: 1.5rem; }
+.toc-level-4 { padding-left: 2rem; font-size: 0.8125rem; }
+.toc-level-5 { padding-left: 2.5rem; font-size: 0.8125rem; opacity: 0.85; }
+.toc-level-6 { padding-left: 3rem; font-size: 0.8125rem; opacity: 0.8; }
+
+/* Main article column */
+.article-main { min-width: 0; max-width: 95ch; }
+.article-content { margin-bottom: 3rem; }
+
+/* Footer */
+.article-footer {
+  border-top: 2px solid var(--color-border);
+  padding-top: 2rem;
+  margin-top: 3rem;
+}
+.article-footer h3 { margin: 0 0 1.5rem 0; color: var(--color-primary); }
+.footer-actions-grid { display: flex; flex-wrap: wrap; gap: 1rem; margin-bottom: 2rem; }
+.footer-actions-grid .btn { flex: 1; min-width: 200px; }
+
+/* Tool button variants (keep since template uses them) */
+.btn-concept { background-color: var(--color-concept); color: white; border-color: var(--color-concept); }
+.btn-concept:hover { opacity: 0.9; }
+.btn-method { background-color: var(--color-method); color: white; border-color: var(--color-method); }
+.btn-method:hover { opacity: 0.9; }
+
+.related-tools { margin-top: 2rem; padding-top: 2rem; border-top: 1px solid var(--color-border); }
+.related-tools h3 { margin: 0 0 1rem 0; color: var(--color-text); }
+.related-tools-grid {
+  display: grid; grid-template-columns: repeat(auto-fill, minmax(200px, 1fr)); gap: 1rem;
+}
+.related-tool-card {
+  display: flex; align-items: center; gap: 0.75rem; padding: 1rem;
+  background-color: var(--color-bg-secondary); border: 1px solid var(--color-border);
+  border-radius: 0.5rem; color: var(--color-text); text-decoration: none;
+  transition: var(--ease-quick);
+}
+.related-tool-card:hover {
+  background-color: var(--color-bg-tertiary); border-color: var(--color-primary);
+  transform: translateY(-1px); box-shadow: var(--shadow-sm);
+}
+.tool-icon { font-size: 1.25rem; flex-shrink: 0; }
+.tool-name { font-weight: 500; font-size: 0.9375rem; }
+
+/* ==========================================================================
+   6) RESPONSIVE
+   ========================================================================== */
+
+@media (max-width: 1200px) {
+  .article-content-wrapper { grid-template-columns: 240px 1fr; gap: 2rem; }
+}
+
+@media (max-width: 1024px) {
+  .article-content-wrapper { grid-template-columns: 1fr; gap: 0; }
+  .article-sidebar { position: static; max-height: none; margin-bottom: 2rem; }
+  .sidebar-toc { max-width: 500px; margin: 0 auto; }
+  .article-main { max-width: 100%; }
+}
+
+@media (max-width: 768px) {
+  .article-layout { padding: 0 0.5rem; }
+  .article-header-content { padding: 1.5rem; }
+  .article-title { font-size: 2rem; }
+  .article-description { font-size: 1.125rem; }
+  .article-metadata-grid { grid-template-columns: 1fr; gap: 1rem; padding: 1rem; }
+  .article-actions { flex-direction: column; }
+  .footer-actions-grid { flex-direction: column; }
+  .footer-actions-grid .btn { min-width: auto; }
+
+  :where(.markdown-content) {
+    font-size: 1rem;
+    line-height: 1.65;
+    max-width: 100%;
+  }
+  :where(.markdown-content) pre { padding: 1rem; margin: 1.5rem 0; font-size: 0.8125rem; }
+  :where(.markdown-content) .callout { padding: 1rem; margin: 1.5rem 0; }
+  :where(.markdown-content) .step { padding: 1rem; margin: 1rem 0; }
+  :where(.markdown-content) .step::before { left: -0.5rem; top: 0.75rem; }
+}
+
+@media (max-width: 480px) {
+  .article-title { font-size: 1.75rem; }
+  .article-icon { font-size: 1.5rem; margin-right: 0.75rem; }
+  .breadcrumb { font-size: 0.8125rem; }
+}
+
+/* ==========================================================================
+   7) PRINT
+   ========================================================================== */
+
+@media print {
+  /* Hide interactive chrome not used in print */
+  .article-sidebar { display: none !important; }
+  .article-actions,
+  .article-footer .footer-actions-grid { display: none !important; }
+
+  /* Expand content */
+  .article-main { max-width: 100% !important; }
+}
diff --git a/src/styles/palette.css b/src/styles/palette.css
new file mode 100644
index 0000000..60affb9
--- /dev/null
+++ b/src/styles/palette.css
@@ -0,0 +1,62 @@
+:root {
+  /* Light Theme Colors */
+  --color-bg: #fff;
+  --color-bg-secondary: #f8fafc;
+  --color-bg-tertiary: #e2e8f0;
+  --color-text: #1e293b;
+  --color-text-secondary: #64748b;
+  --color-border: #cbd5e1;
+  --color-primary: #2563eb;
+  --color-primary-hover: #1d4ed8;
+  --color-accent: #059669;
+  --color-accent-hover: #047857;
+  --color-warning: #d97706;
+  --color-error: #dc2626;
+  
+  /* Enhanced card type colors */
+  --color-hosted: #7c3aed;
+  --color-hosted-bg: #f3f0ff;
+  --color-oss: #059669;
+  --color-oss-bg: #ecfdf5;
+  --color-method: #0891b2;
+  --color-method-bg: #f0f9ff;
+  --color-concept: #ea580c;
+  --color-concept-bg: #fff7ed;
+  
+  /* Shadows */
+  --shadow-sm: 0 1px 2px 0 rgb(0 0 0 / 5%);
+  --shadow-md: 0 4px 6px -1px rgb(0 0 0 / 10%);
+  --shadow-lg: 0 10px 15px -3px rgb(0 0 0 / 10%);
+  
+  /* Transitions */
+  --transition-fast: all 0.2s ease;
+  --transition-medium: all 0.3s ease;
+}
+
+[data-theme="dark"] {
+  --color-bg: #0f172a;
+  --color-bg-secondary: #1e293b;
+  --color-bg-tertiary: #334155;
+  --color-text: #f1f5f9;
+  --color-text-secondary: #94a3b8;
+  --color-border: #475569;
+  --color-primary: #3b82f6;
+  --color-primary-hover: #60a5fa;
+  --color-accent: #10b981;
+  --color-accent-hover: #34d399;
+  --color-warning: #f59e0b;
+  --color-error: #f87171;
+  
+  --color-hosted: #a855f7;
+  --color-hosted-bg: #2e1065;
+  --color-oss: #10b981;
+  --color-oss-bg: #064e3b;
+  --color-method: #0891b2;
+  --color-method-bg: #164e63;
+  --color-concept: #f97316;
+  --color-concept-bg: #7c2d12;
+  
+  --shadow-sm: 0 1px 2px 0 rgb(0 0 0 / 30%);
+  --shadow-md: 0 4px 6px -1px rgb(0 0 0 / 40%);
+  --shadow-lg: 0 10px 15px -3px rgb(0 0 0 / 50%);
+}
\ No newline at end of file
diff --git a/src/utils/aiPipeline.ts b/src/utils/aiPipeline.ts
index c1583c0..2bf712d 100644
--- a/src/utils/aiPipeline.ts
+++ b/src/utils/aiPipeline.ts
@@ -1,9 +1,12 @@
-// src/utils/aiPipeline.ts - Enhanced with Proper Confidence Scoring
+// src/utils/aiPipeline.ts
 
 import { getCompressedToolsDataForAI } from './dataService.js';
 import { embeddingsService, type EmbeddingData, type SimilarityResult } from './embeddings.js';
 import { AI_PROMPTS, getPrompt } from '../config/prompts.js';
 import { isToolHosted } from './toolHelpers.js';
+import dotenv from 'dotenv';
+
+dotenv.config();
 
 interface AIConfig {
   endpoint: string;
@@ -34,11 +37,11 @@ interface AnalysisResult {
 
 interface AuditEntry {
   timestamp: number;
-  phase: string;           
-  action: string;          
-  input: any;              
-  output: any;             
-  confidence: number;      
+  phase: string;
+  action: string;
+  input: any;
+  output: any;
+  confidence: number;
   processingTimeMs: number;
   metadata: Record<string, any>;
 }
@@ -48,30 +51,37 @@ interface AnalysisContext {
   mode: string;
   filteredData: any;
   contextHistory: string[];
-  
   maxContextLength: number;
   currentContextLength: number;
-  
   scenarioAnalysis?: string;
   problemAnalysis?: string;
   investigationApproach?: string;
   criticalConsiderations?: string;
-  selectedTools?: Array<{tool: any, phase: string, priority: string, justification?: string, taskRelevance?: number, limitations?: string[]}>;
-  backgroundKnowledge?: Array<{concept: any, relevance: string}>;
-  
+  selectedTools?: Array<{
+    tool: any;
+    phase: string;
+    priority: string;
+    justification?: string;
+    taskRelevance?: number;
+    limitations?: string[];
+  }>;
+  backgroundKnowledge?: Array<{
+    concept: any;
+    relevance: string;
+  }>;
   seenToolNames: Set<string>;
-  
   auditTrail: AuditEntry[];
-  
   embeddingsSimilarities: Map<string, number>;
+  aiSelectedTools?: any[];
+  aiSelectedConcepts?: any[];
 }
 
 interface ConfidenceMetrics {
-  overall: number;                    
-  semanticRelevance: number;         
-  taskSuitability: number;          
-  uncertaintyFactors: string[];   
-  strengthIndicators: string[]; 
+  overall: number;
+  semanticRelevance: number;
+  taskSuitability: number;
+  uncertaintyFactors: string[];
+  strengthIndicators: string[];
 }
 
 class ImprovedMicroTaskAIPipeline {
@@ -80,173 +90,141 @@ class ImprovedMicroTaskAIPipeline {
   private embeddingCandidates: number;
   private similarityThreshold: number;
   private microTaskDelay: number;
-  
   private embeddingSelectionLimit: number;
   private embeddingConceptsLimit: number;
-
   private noEmbeddingsToolLimit: number;
   private noEmbeddingsConceptLimit: number;
-  
   private embeddingsMinTools: number;
   private embeddingsMaxReductionRatio: number;
-  
+  private methodSelectionRatio: number;
+  private softwareSelectionRatio: number;
   private maxContextTokens: number;
   private maxPromptTokens: number;
-  
   private auditConfig: {
     enabled: boolean;
-    detailLevel: 'minimal' | 'standard' | 'verbose';
-    retentionHours: number;
+    detailLevel: string;
   };
-
   private confidenceConfig: {
-    semanticWeight: number;       
-    suitabilityWeight: number;  
-    consistencyWeight: number;    
-    reliabilityWeight: number;    
+    semanticWeight: number;
+    suitabilityWeight: number;
     minimumThreshold: number;
     mediumThreshold: number;
     highThreshold: number;
   };
-  
-  private tempAuditEntries: AuditEntry[] = [];
 
   constructor() {
     this.config = {
-      endpoint: this.getEnv('AI_ANALYZER_ENDPOINT'),
-      apiKey: this.getEnv('AI_ANALYZER_API_KEY'), 
-      model: this.getEnv('AI_ANALYZER_MODEL')
+      endpoint: this.getRequiredEnv('AI_ANALYZER_ENDPOINT'),
+      apiKey: this.getRequiredEnv('AI_ANALYZER_API_KEY'),
+      model: this.getRequiredEnv('AI_ANALYZER_MODEL')
     };
 
-    this.maxSelectedItems = parseInt(process.env.AI_MAX_SELECTED_ITEMS || '25', 10);
-    this.embeddingCandidates = parseInt(process.env.AI_EMBEDDING_CANDIDATES || '50', 10); 
-    this.similarityThreshold = parseFloat(process.env.AI_SIMILARITY_THRESHOLD || '0.3');
-    this.microTaskDelay = parseInt(process.env.AI_MICRO_TASK_DELAY_MS || '500', 10);
-    
-    this.embeddingSelectionLimit = parseInt(process.env.AI_EMBEDDING_SELECTION_LIMIT || '30', 10);
-    this.embeddingConceptsLimit = parseInt(process.env.AI_EMBEDDING_CONCEPTS_LIMIT || '15', 10);
+    this.maxSelectedItems = this.getEnvInt('AI_MAX_SELECTED_ITEMS', 25);
+    this.embeddingCandidates = this.getEnvInt('AI_EMBEDDING_CANDIDATES', 50);
+    this.similarityThreshold = this.getEnvFloat('AI_SIMILARITY_THRESHOLD', 0.3);
+    this.microTaskDelay = this.getEnvInt('AI_MICRO_TASK_DELAY_MS', 500);
+    this.embeddingSelectionLimit = this.getEnvInt('AI_EMBEDDING_SELECTION_LIMIT', 30);
+    this.embeddingConceptsLimit = this.getEnvInt('AI_EMBEDDING_CONCEPTS_LIMIT', 15);
+    this.noEmbeddingsToolLimit = this.getEnvInt('AI_NO_EMBEDDINGS_TOOL_LIMIT', 25);
+    this.noEmbeddingsConceptLimit = this.getEnvInt('AI_NO_EMBEDDINGS_CONCEPT_LIMIT', 10);
+    this.embeddingsMinTools = this.getEnvInt('AI_EMBEDDINGS_MIN_TOOLS', 8);
+    this.embeddingsMaxReductionRatio = this.getEnvFloat('AI_EMBEDDINGS_MAX_REDUCTION_RATIO', 0.75);
+    this.methodSelectionRatio = this.getEnvFloat('AI_METHOD_SELECTION_RATIO', 0.4);
+    this.softwareSelectionRatio = this.getEnvFloat('AI_SOFTWARE_SELECTION_RATIO', 0.5);
+    this.maxContextTokens = this.getEnvInt('AI_MAX_CONTEXT_TOKENS', 4000);
+    this.maxPromptTokens = this.getEnvInt('AI_MAX_PROMPT_TOKENS', 1500);
 
-    this.noEmbeddingsToolLimit = parseInt(process.env.AI_NO_EMBEDDINGS_TOOL_LIMIT || '0', 10);
-    this.noEmbeddingsConceptLimit = parseInt(process.env.AI_NO_EMBEDDINGS_CONCEPT_LIMIT || '0', 10);
-    
-    this.embeddingsMinTools = parseInt(process.env.AI_EMBEDDINGS_MIN_TOOLS || '8', 10);
-    this.embeddingsMaxReductionRatio = parseFloat(process.env.AI_EMBEDDINGS_MAX_REDUCTION_RATIO || '0.75');
-    
-    this.maxContextTokens = parseInt(process.env.AI_MAX_CONTEXT_TOKENS || '4000', 10);
-    this.maxPromptTokens = parseInt(process.env.AI_MAX_PROMPT_TOKENS || '1500', 10);
-    
     this.auditConfig = {
       enabled: process.env.FORENSIC_AUDIT_ENABLED === 'true',
-      detailLevel: (process.env.FORENSIC_AUDIT_DETAIL_LEVEL as any) || 'standard',
-      retentionHours: parseInt(process.env.FORENSIC_AUDIT_RETENTION_HOURS || '72', 10)
-    };
-    
-    this.confidenceConfig = {
-      semanticWeight: parseFloat(process.env.CONFIDENCE_SEMANTIC_WEIGHT || '0.3'),   
-      suitabilityWeight: parseFloat(process.env.CONFIDENCE_SUITABILITY_WEIGHT || '0.7'), 
-      consistencyWeight: 0,    
-      reliabilityWeight: 0,
-      minimumThreshold: parseInt(process.env.CONFIDENCE_MINIMUM_THRESHOLD || '40', 10),
-      mediumThreshold: parseInt(process.env.CONFIDENCE_MEDIUM_THRESHOLD || '60', 10),
-      highThreshold: parseInt(process.env.CONFIDENCE_HIGH_THRESHOLD || '80', 10)
+      detailLevel: process.env.FORENSIC_AUDIT_DETAIL_LEVEL || 'standard'
     };
 
-    console.log('[AI PIPELINE] Simplified confidence scoring enabled:', {
-      weights: `Semantic:${this.confidenceConfig.semanticWeight} Suitability:${this.confidenceConfig.suitabilityWeight}`,
-      thresholds: `${this.confidenceConfig.minimumThreshold}/${this.confidenceConfig.mediumThreshold}/${this.confidenceConfig.highThreshold}`
-    });
+    this.confidenceConfig = {
+      semanticWeight: this.getEnvFloat('CONFIDENCE_SEMANTIC_WEIGHT', 0.3),
+      suitabilityWeight: this.getEnvFloat('CONFIDENCE_SUITABILITY_WEIGHT', 0.7),
+      minimumThreshold: this.getEnvInt('CONFIDENCE_MINIMUM_THRESHOLD', 40),
+      mediumThreshold: this.getEnvInt('CONFIDENCE_MEDIUM_THRESHOLD', 60),
+      highThreshold: this.getEnvInt('CONFIDENCE_HIGH_THRESHOLD', 80)
+    };
+
+    this.logPipelineInit();
   }
 
-  private getEnv(key: string): string {
+  private getRequiredEnv(key: string): string {
     const value = process.env[key];
     if (!value) {
-      throw new Error(`Missing environment variable: ${key}`);
+      throw new Error(`Missing required environment variable: ${key}`);
     }
     return value;
   }
 
+  private getEnvInt(key: string, defaultValue: number): number {
+    const value = process.env[key];
+    return value ? parseInt(value, 10) : defaultValue;
+  }
+
+  private getEnvFloat(key: string, defaultValue: number): number {
+    const value = process.env[key];
+    return value ? parseFloat(value) : defaultValue;
+  }
+
+  private logPipelineInit(): void {
+    console.log('[AI-PIPELINE] Initialized with audit:', this.auditConfig.enabled);
+    console.log('[AI-PIPELINE] Method/Software balance:', 
+      `${(this.methodSelectionRatio * 100).toFixed(0)}%/${(this.softwareSelectionRatio * 100).toFixed(0)}%`);
+  }
+
   private addAuditEntry(
-    context: AnalysisContext | null, 
-    phase: string, 
-    action: string, 
-    input: any, 
-    output: any, 
-    confidence: number, 
-    startTime: number, 
+    context: AnalysisContext,
+    phase: string,
+    action: string,
+    input: any,
+    output: any,
+    confidence: number,
+    startTime: number,
     metadata: Record<string, any> = {}
   ): void {
     if (!this.auditConfig.enabled) return;
-    
-    const auditEntry: AuditEntry = {
+
+    const entry: AuditEntry = {
       timestamp: Date.now(),
       phase,
-      action, 
-      input: this.auditConfig.detailLevel === 'verbose' ? input : this.summarizeForAudit(input),
-      output: this.auditConfig.detailLevel === 'verbose' ? output : this.summarizeForAudit(output),
-      confidence,
+      action,
+      input,
+      output,
+      confidence: Math.round(confidence),
       processingTimeMs: Date.now() - startTime,
       metadata
     };
-    
-    if (context) {
-      context.auditTrail.push(auditEntry);
-    } else {
-      this.tempAuditEntries.push(auditEntry);
-    }
-    
-    console.log(`[AUDIT] ${phase}/${action}: ${confidence}% confidence, ${Date.now() - startTime}ms`);
-  }
-  
-  private mergeTemporaryAuditEntries(context: AnalysisContext): void {
-    if (!this.auditConfig.enabled || this.tempAuditEntries.length === 0) return;
-    
-    const entryCount = this.tempAuditEntries.length;
-    context.auditTrail.unshift(...this.tempAuditEntries);
-    this.tempAuditEntries = []; 
-    
-    console.log(`[AUDIT] Merged ${entryCount} temporary audit entries into context`);
+
+    context.auditTrail.push(entry);
+    this.logAuditEntry(phase, action, confidence, entry.processingTimeMs);
   }
 
-  private summarizeForAudit(data: any): any {
-    if (this.auditConfig.detailLevel === 'minimal') {
-      if (typeof data === 'string' && data.length > 100) {
-        return data.slice(0, 100) + '...[truncated]';
-      }
-      if (Array.isArray(data) && data.length > 3) {
-        return [...data.slice(0, 3), `...[${data.length - 3} more items]`];
-      }
-    } else if (this.auditConfig.detailLevel === 'standard') {
-      if (typeof data === 'string' && data.length > 500) {
-        return data.slice(0, 500) + '...[truncated]';
-      }
-      if (Array.isArray(data) && data.length > 10) {
-        return [...data.slice(0, 10), `...[${data.length - 10} more items]`];
-      }
-    }
-    return data;
+  private logAuditEntry(phase: string, action: string, confidence: number, timeMs: number): void {
+    console.log(`[AUDIT] ${phase}/${action}: ${confidence}% confidence, ${timeMs}ms`);
   }
 
   private calculateSelectionConfidence(result: any, candidateCount: number): number {
-    if (!result || !result.selectedTools) return 30;
+    if (!result?.selectedTools) return 30;
     
     const selectionRatio = result.selectedTools.length / candidateCount;
     const hasReasoning = result.reasoning && result.reasoning.length > 50;
     
-    let confidence = 60; 
+    let confidence = 60;
     
     if (selectionRatio > 0.05 && selectionRatio < 0.3) confidence += 20;
-    else if (selectionRatio <= 0.05) confidence -= 10; 
-    else confidence -= 15; 
+    else if (selectionRatio <= 0.05) confidence -= 10;
+    else confidence -= 15;
     
     if (hasReasoning) confidence += 15;
-    
-    if (result.selectedConcepts && result.selectedConcepts.length > 0) confidence += 5;
+    if (result.selectedConcepts?.length > 0) confidence += 5;
     
     return Math.min(95, Math.max(25, confidence));
   }
 
   private estimateTokens(text: string): number {
-    return Math.ceil(text.length / 4); 
+    return Math.ceil(text.length / 4);
   }
 
   private addToContextHistory(context: AnalysisContext, newEntry: string): void {
@@ -263,19 +241,30 @@ class ImprovedMicroTaskAIPipeline {
 
   private safeParseJSON(jsonString: string, fallback: any = null): any {
     try {
-      let cleaned = jsonString
-        .replace(/^```json\s*/i, '')
-        .replace(/\s*```\s*$/g, '')
-        .trim();
+      let cleaned = jsonString.trim();
+      
+      const jsonBlockPatterns = [
+        /```json\s*([\s\S]*?)\s*```/i,
+        /```\s*([\s\S]*?)\s*```/i,
+        /\{[\s\S]*\}/,
+      ];
+      
+      for (const pattern of jsonBlockPatterns) {
+        const match = cleaned.match(pattern);
+        if (match) {
+          cleaned = match[1] || match[0];
+          break;
+        }
+      }
       
       if (!cleaned.endsWith('}') && !cleaned.endsWith(']')) {
-        console.warn('[AI PIPELINE] JSON appears truncated, attempting recovery...');
+        console.warn('[AI-PIPELINE] JSON appears truncated, attempting recovery');
         
-        let lastCompleteStructure = '';
         let braceCount = 0;
         let bracketCount = 0;
         let inString = false;
         let escaped = false;
+        let lastCompleteStructure = '';
         
         for (let i = 0; i < cleaned.length; i++) {
           const char = cleaned[i];
@@ -308,61 +297,87 @@ class ImprovedMicroTaskAIPipeline {
         }
         
         if (lastCompleteStructure) {
-          console.log('[AI PIPELINE] Attempting to parse recovered JSON structure...');
           cleaned = lastCompleteStructure;
         } else {
-          if (braceCount > 0) {
-            cleaned += '}';
-            console.log('[AI PIPELINE] Added closing brace to truncated JSON');
-          }
-          if (bracketCount > 0) {
-            cleaned += ']';
-            console.log('[AI PIPELINE] Added closing bracket to truncated JSON');
-          }
+          if (braceCount > 0) cleaned += '}';
+          if (bracketCount > 0) cleaned += ']';
         }
       }
       
       const parsed = JSON.parse(cleaned);
       
       if (parsed && typeof parsed === 'object') {
-        if (parsed.selectedTools === undefined) parsed.selectedTools = [];
-        if (parsed.selectedConcepts === undefined) parsed.selectedConcepts = [];
-        
+        if (!parsed.selectedTools) parsed.selectedTools = [];
+        if (!parsed.selectedConcepts) parsed.selectedConcepts = [];
         if (!Array.isArray(parsed.selectedTools)) parsed.selectedTools = [];
         if (!Array.isArray(parsed.selectedConcepts)) parsed.selectedConcepts = [];
       }
       
       return parsed;
-    } catch (error) {
-      console.warn('[AI PIPELINE] JSON parsing failed:', error.message);
-      console.warn('[AI PIPELINE] Raw content (first 300 chars):', jsonString.slice(0, 300));
-      console.warn('[AI PIPELINE] Raw content (last 300 chars):', jsonString.slice(-300));
       
-      if (jsonString.includes('selectedTools')) {
-        const toolMatches = jsonString.match(/"([^"]+)"/g);
-        if (toolMatches && toolMatches.length > 0) {
-          console.log('[AI PIPELINE] Attempting partial recovery from broken JSON...');
-          const possibleTools = toolMatches
-            .map(match => match.replace(/"/g, ''))
-            .filter(name => name.length > 2 && !['selectedTools', 'selectedConcepts', 'reasoning'].includes(name))
-            .slice(0, 15); 
-          
-          if (possibleTools.length > 0) {
-            console.log(`[AI PIPELINE] Recovered ${possibleTools.length} possible tool names from broken JSON`);
-            return {
-              selectedTools: possibleTools,
-              selectedConcepts: [],
-              reasoning: 'Recovered from truncated response'
-            };
+    } catch (error) {
+      console.warn('[AI-PIPELINE] JSON parsing failed:', error.message);
+      
+      if (jsonString.includes('selectedTools') || jsonString.includes('selectedConcepts')) {
+        const selectedTools: string[] = [];
+        const selectedConcepts: string[] = [];
+        
+        const toolsMatch = jsonString.match(/"selectedTools"\s*:\s*\[([\s\S]*?)\]/i);
+        if (toolsMatch) {
+          const toolMatches = toolsMatch[1].match(/"([^"]+)"/g);
+          if (toolMatches) {
+            selectedTools.push(...toolMatches.map(match => match.replace(/"/g, '')));
           }
         }
+        
+        const conceptsMatch = jsonString.match(/"selectedConcepts"\s*:\s*\[([\s\S]*?)\]/i);
+        if (conceptsMatch) {
+          const conceptMatches = conceptsMatch[1].match(/"([^"]+)"/g);
+          if (conceptMatches) {
+            selectedConcepts.push(...conceptMatches.map(match => match.replace(/"/g, '')));
+          }
+        }
+        
+        if (selectedTools.length === 0 && selectedConcepts.length === 0) {
+          const allMatches = jsonString.match(/"([^"]+)"/g);
+          if (allMatches) {
+            const possibleNames = allMatches
+              .map(match => match.replace(/"/g, ''))
+              .filter(name => 
+                name.length > 2 && 
+                !['selectedTools', 'selectedConcepts', 'reasoning'].includes(name) &&
+                !name.includes(':') &&
+                !name.match(/^\d+$/)
+              )
+              .slice(0, 15);
+            
+            selectedTools.push(...possibleNames);
+          }
+        }
+        
+        if (selectedTools.length > 0 || selectedConcepts.length > 0) {
+          console.log('[AI-PIPELINE] JSON recovery successful:', selectedTools.length, 'tools,', selectedConcepts.length, 'concepts');
+          return {
+            selectedTools,
+            selectedConcepts,
+            reasoning: 'Recovered from malformed JSON response'
+          };
+        }
       }
       
       return fallback;
     }
   }
 
-  private addToolToSelection(context: AnalysisContext, tool: any, phase: string, priority: string, justification?: string, taskRelevance?: number, limitations?: string[]): boolean {   
+  private addToolToSelection(
+    context: AnalysisContext,
+    tool: any,
+    phase: string,
+    priority: string,
+    justification?: string,
+    taskRelevance?: number,
+    limitations?: string[]
+  ): boolean {
     context.seenToolNames.add(tool.name);
     if (!context.selectedTools) context.selectedTools = [];
     
@@ -378,51 +393,70 @@ class ImprovedMicroTaskAIPipeline {
     return true;
   }
 
-  private async getIntelligentCandidates(userQuery: string, toolsData: any, mode: string, context: AnalysisContext) {
+  private generatePhaseQueryTemplates(phases: any[]): Record<string, string> {
+    const templates: Record<string, string> = {};
+    
+    phases.forEach((phase: any) => {
+      if (phase?.id && phase?.name) {
+        const phaseKeywords = [
+          'forensic',
+          phase.name.toLowerCase(),
+          ...(phase.description ? phase.description.toLowerCase().split(' ').filter((word: string) => word.length > 3) : []),
+          ...(phase.key_activities || []).map((activity: string) => activity.toLowerCase()),
+          ...(phase.typical_tools || []).map((tool: string) => tool.toLowerCase())
+        ].join(' ');
+        
+        templates[phase.id] = phaseKeywords;
+      }
+    });
+    
+    return templates;
+  }
+
+  private async getIntelligentCandidates(
+    userQuery: string,
+    toolsData: any,
+    mode: string,
+    context: AnalysisContext
+  ) {
     let candidateTools: any[] = [];
     let candidateConcepts: any[] = [];
     let selectionMethod = 'unknown';
     
     context.embeddingsSimilarities = new Map<string, number>();
     
-    if (process.env.AI_EMBEDDINGS_ENABLED === 'true') {
-      try {
-        console.log('[AI PIPELINE] Waiting for embeddings initialization...');
-        await embeddingsService.waitForInitialization();
-        console.log('[AI PIPELINE] Embeddings ready, proceeding with similarity search');
-      } catch (error) {
-        console.error('[AI PIPELINE] Embeddings initialization failed, falling back to full dataset:', error);
-      }
+    try {
+      await embeddingsService.waitForInitialization();
+    } catch (error) {
+      console.error('[AI-PIPELINE] Embeddings initialization failed:', error);
     }
     
     if (embeddingsService.isEnabled()) {
       const embeddingsStart = Date.now();
       const similarItems = await embeddingsService.findSimilar(
-        userQuery, 
-        this.embeddingCandidates, 
+        userQuery,
+        this.embeddingCandidates,
         this.similarityThreshold
       ) as SimilarityResult[];
       
-      console.log(`[AI PIPELINE] Embeddings found ${similarItems.length} similar items`);
+      console.log('[AI-PIPELINE] Embeddings found', similarItems.length, 'similar items');
       
       similarItems.forEach(item => {
         context.embeddingsSimilarities.set(item.name, item.similarity);
       });
       
-      const toolsMap = new Map<string, any>(toolsData.tools.map((tool: any) => [tool.name, tool]));
-      const conceptsMap = new Map<string, any>(toolsData.concepts.map((concept: any) => [concept.name, concept]));
+      const toolsMap = new Map(toolsData.tools.map((tool: any) => [tool.name, tool]));
+      const conceptsMap = new Map(toolsData.concepts.map((concept: any) => [concept.name, concept]));
       
       const similarTools = similarItems
-        .filter((item): item is SimilarityResult => item.type === 'tool')
-        .map(item => toolsMap.get(item.name))
-        .filter((tool): tool is any => tool !== undefined);
+        .filter((item: any) => item.type === 'tool')
+        .map((item: any) => toolsMap.get(item.name))
+        .filter((tool: any): tool is NonNullable<any> => tool !== undefined && tool !== null);
       
       const similarConcepts = similarItems
-        .filter((item): item is SimilarityResult => item.type === 'concept')
-        .map(item => conceptsMap.get(item.name))
-        .filter((concept): concept is any => concept !== undefined);
-      
-      console.log(`[AI PIPELINE] Similarity-ordered results: ${similarTools.length} tools, ${similarConcepts.length} concepts`);
+        .filter((item: any) => item.type === 'concept')
+        .map((item: any) => conceptsMap.get(item.name))
+        .filter((concept: any): concept is NonNullable<any> => concept !== undefined && concept !== null);
       
       const totalAvailableTools = toolsData.tools.length;
       const reductionRatio = similarTools.length / totalAvailableTools;
@@ -432,50 +466,47 @@ class ImprovedMicroTaskAIPipeline {
         candidateConcepts = similarConcepts;
         selectionMethod = 'embeddings_candidates';
         
-        console.log(`[AI PIPELINE] Using embeddings filtering: ${totalAvailableTools} → ${similarTools.length} tools (${(reductionRatio * 100).toFixed(1)}% reduction)`);
+        console.log('[AI-PIPELINE] Using embeddings filtering:', totalAvailableTools, '→', similarTools.length, 'tools');
       } else {
-        if (similarTools.length < this.embeddingsMinTools) {
-          console.log(`[AI PIPELINE] Embeddings found too few tools (${similarTools.length} < ${this.embeddingsMinTools}), using full dataset`);
-        } else {
-          console.log(`[AI PIPELINE] Embeddings didn't filter enough (${(reductionRatio * 100).toFixed(1)}% > ${(this.embeddingsMaxReductionRatio * 100).toFixed(1)}%), using full dataset`);
-        }
+        console.log('[AI-PIPELINE] Embeddings filtering insufficient, using full dataset');
         candidateTools = toolsData.tools;
         candidateConcepts = toolsData.concepts;
         selectionMethod = 'full_dataset';
       }
       
       if (this.auditConfig.enabled) {
-        this.addAuditEntry(context, 'retrieval', 'embeddings-search', 
-          { query: userQuery, threshold: this.similarityThreshold, candidates: this.embeddingCandidates }, 
-          { 
-            candidatesFound: similarItems.length, 
-            toolsInOrder: similarTools.slice(0, 3).map((t: any) => t.name),
-            conceptsInOrder: similarConcepts.slice(0, 3).map((c: any) => c.name),
+        this.addAuditEntry(
+          context,
+          'retrieval',
+          'embeddings-search',
+          { query: userQuery, threshold: this.similarityThreshold, candidates: this.embeddingCandidates },
+          {
+            candidatesFound: similarItems.length,
             reductionRatio: reductionRatio,
             usingEmbeddings: selectionMethod === 'embeddings_candidates',
             totalAvailable: totalAvailableTools,
-            filtered: similarTools.length,
-            avgSimilarity: similarItems.length > 0 ? similarItems.reduce((sum, item) => sum + item.similarity, 0) / similarItems.length : 0
+            filtered: similarTools.length
           },
           selectionMethod === 'embeddings_candidates' ? 85 : 60,
           embeddingsStart,
-          { 
-            selectionMethod, 
-            embeddingsEnabled: true, 
-            reductionAchieved: selectionMethod === 'embeddings_candidates',
-            tokenSavingsExpected: selectionMethod === 'embeddings_candidates'
-          }
+          { selectionMethod, embeddingsEnabled: true }
         );
       }
     } else {
-      console.log(`[AI PIPELINE] Embeddings disabled or not ready, using full dataset`);
+      console.log('[AI-PIPELINE] Embeddings disabled, using full dataset');
       candidateTools = toolsData.tools;
       candidateConcepts = toolsData.concepts;
       selectionMethod = 'full_dataset';
     }
 
-    console.log(`[AI PIPELINE] AI will analyze ${candidateTools.length} candidate tools (method: ${selectionMethod})`);
-    const finalSelection = await this.aiSelectionWithFullData(userQuery, candidateTools, candidateConcepts, mode, selectionMethod, context);
+    const finalSelection = await this.aiSelectionWithFullData(
+      userQuery,
+      candidateTools,
+      candidateConcepts,
+      mode,
+      selectionMethod,
+      context
+    );
     
     return {
       tools: finalSelection.selectedTools,
@@ -487,159 +518,204 @@ class ImprovedMicroTaskAIPipeline {
   }
 
   private async aiSelectionWithFullData(
-    userQuery: string, 
-    candidateTools: any[], 
-    candidateConcepts: any[], 
+    userQuery: string,
+    candidateTools: any[],
+    candidateConcepts: any[],
     mode: string,
     selectionMethod: string,
     context: AnalysisContext
   ) {
     const selectionStart = Date.now();
     
-    const toolsWithFullData = candidateTools.map((tool: any) => ({
-      name: tool.name,
-      type: tool.type,
-      description: tool.description,
-      domains: tool.domains,
-      phases: tool.phases,
-      platforms: tool.platforms || [],
-      tags: tool.tags || [],
-      skillLevel: tool.skillLevel,
-      license: tool.license,
-      accessType: tool.accessType,
-      projectUrl: tool.projectUrl,
-      knowledgebase: tool.knowledgebase,
-      related_concepts: tool.related_concepts || [],
-      related_software: tool.related_software || []
-    }));
-
-    const conceptsWithFullData = candidateConcepts.map((concept: any) => ({
-      name: concept.name,
-      type: 'concept',
-      description: concept.description,
-      domains: concept.domains,
-      phases: concept.phases,
-      tags: concept.tags || [],
-      skillLevel: concept.skillLevel,
-      related_concepts: concept.related_concepts || [],
-      related_software: concept.related_software || []
-    }));
+    const candidateMethods = candidateTools.filter((tool: any) => tool && tool.type === 'method');
+    const candidateSoftware = candidateTools.filter((tool: any) => tool && tool.type === 'software');
+    
+    console.log('[AI-PIPELINE] Tool selection candidates:', candidateMethods.length, 'methods,', candidateSoftware.length, 'software,', candidateConcepts.length, 'concepts');
+    
+    const methodsWithFullData = candidateMethods.map(this.createToolData);
+    const softwareWithFullData = candidateSoftware.map(this.createToolData);
+    const conceptsWithFullData = candidateConcepts.map(this.createConceptData);
 
     let toolsToSend: any[];
     let conceptsToSend: any[];
     
     if (selectionMethod === 'embeddings_candidates') {
-      toolsToSend = toolsWithFullData.slice(0, this.embeddingSelectionLimit);
+      const totalLimit = this.embeddingSelectionLimit;
+      const methodLimit = Math.ceil(totalLimit * this.methodSelectionRatio);
+      const softwareLimit = Math.floor(totalLimit * this.softwareSelectionRatio);
+      
+      toolsToSend = [
+        ...methodsWithFullData.slice(0, methodLimit),
+        ...softwareWithFullData.slice(0, softwareLimit)
+      ];
+      
+      const remainingCapacity = totalLimit - toolsToSend.length;
+      if (remainingCapacity > 0) {
+        if (methodsWithFullData.length > methodLimit) {
+          toolsToSend.push(...methodsWithFullData.slice(methodLimit, methodLimit + remainingCapacity));
+        } else if (softwareWithFullData.length > softwareLimit) {
+          toolsToSend.push(...softwareWithFullData.slice(softwareLimit, softwareLimit + remainingCapacity));
+        }
+      }
+      
       conceptsToSend = conceptsWithFullData.slice(0, this.embeddingConceptsLimit);
-      
-      console.log(`[AI PIPELINE] Embeddings enabled: sending top ${toolsToSend.length} similarity-ordered tools`);
     } else {
-      const maxTools = this.noEmbeddingsToolLimit > 0 ? 
-        Math.min(this.noEmbeddingsToolLimit, candidateTools.length) : 
-        candidateTools.length;
+      const maxTools = this.noEmbeddingsToolLimit;
+      const maxConcepts = this.noEmbeddingsConceptLimit;
+      const methodLimit = Math.ceil(maxTools * 0.4);
+      const softwareLimit = Math.floor(maxTools * 0.5);
       
-      const maxConcepts = this.noEmbeddingsConceptLimit > 0 ? 
-        Math.min(this.noEmbeddingsConceptLimit, candidateConcepts.length) : 
-        candidateConcepts.length;
+      toolsToSend = [
+        ...methodsWithFullData.slice(0, methodLimit),
+        ...softwareWithFullData.slice(0, softwareLimit)
+      ];
+      
+      const remainingCapacity = maxTools - toolsToSend.length;
+      if (remainingCapacity > 0) {
+        if (methodsWithFullData.length > methodLimit) {
+          toolsToSend.push(...methodsWithFullData.slice(methodLimit, methodLimit + remainingCapacity));
+        } else if (softwareWithFullData.length > softwareLimit) {
+          toolsToSend.push(...softwareWithFullData.slice(softwareLimit, softwareLimit + remainingCapacity));
+        }
+      }
       
-      toolsToSend = toolsWithFullData.slice(0, maxTools);
       conceptsToSend = conceptsWithFullData.slice(0, maxConcepts);
-      
-      console.log(`[AI PIPELINE] Embeddings disabled: sending ${toolsToSend.length}/${candidateTools.length} tools (limit: ${this.noEmbeddingsToolLimit || 'none'})`);
     }
 
     const basePrompt = getPrompt('toolSelection', mode, userQuery, selectionMethod, this.maxSelectedItems);
-    const prompt = `${basePrompt}
-
-VERFÜGBARE TOOLS (mit vollständigen Daten):
-${JSON.stringify(toolsToSend, null, 2)}
-
-VERFÜGBARE KONZEPTE (mit vollständigen Daten):
-${JSON.stringify(conceptsToSend, null, 2)}`;
+    const prompt = getPrompt('toolSelectionWithData', basePrompt, toolsToSend, conceptsToSend);
 
     const estimatedTokens = this.estimateTokens(prompt);
-    console.log(`[AI PIPELINE] Method: ${selectionMethod}, Tools: ${toolsToSend.length}, Estimated tokens: ~${estimatedTokens}`);
+    console.log('[AI-PIPELINE] Sending to AI:', toolsToSend.filter((t: any) => t.type === 'method').length, 'methods,', toolsToSend.filter((t: any) => t.type === 'software').length, 'software,', conceptsToSend.length, 'concepts');
 
     if (estimatedTokens > 35000) {
-      console.warn(`[AI PIPELINE] WARNING: Prompt tokens (${estimatedTokens}) may exceed model limits`);
+      console.warn('[AI-PIPELINE] WARNING: Prompt tokens may exceed model limits:', estimatedTokens);
     }
 
     try {
       const response = await this.callAI(prompt, 2500);
-      
       const result = this.safeParseJSON(response, null);
       
       if (!result || !Array.isArray(result.selectedTools) || !Array.isArray(result.selectedConcepts)) {
-        console.error('[AI PIPELINE] AI selection returned invalid structure:', response.slice(0, 200));
+        console.error('[AI-PIPELINE] AI selection returned invalid structure');
         
         if (this.auditConfig.enabled) {
-          this.addAuditEntry(context, 'selection', 'ai-tool-selection-failed',
-            { candidateCount: candidateTools.length, mode, prompt: prompt.slice(0, 200) },
-            { error: 'Invalid JSON structure', response: response.slice(0, 200) },
+          this.addAuditEntry(
+            context,
+            'selection',
+            'ai-tool-selection-failed',
+            { candidateCount: candidateTools.length, mode },
+            { error: 'Invalid JSON structure' },
             10,
             selectionStart,
-            { aiModel: this.config.model, selectionMethod, tokensSent: estimatedTokens, toolsSent: toolsToSend.length }
+            { aiModel: this.config.model, selectionMethod }
           );
         }
         
-        throw new Error('AI selection failed to return valid tool selection');
+        throw new Error('AI selection failed to return valid tool and concept selection');
       }
 
       const totalSelected = result.selectedTools.length + result.selectedConcepts.length;
       if (totalSelected === 0) {
-        console.error('[AI PIPELINE] AI selection returned no tools');
         throw new Error('AI selection returned empty selection');
       }
       
-      console.log(`[AI PIPELINE] AI selected: ${result.selectedTools.length} tools, ${result.selectedConcepts.length} concepts from ${toolsToSend.length} candidates`);
-
-      const selectedTools = candidateTools.filter(tool => result.selectedTools.includes(tool.name));
-      const selectedConcepts = candidateConcepts.filter(concept => result.selectedConcepts.includes(concept.name));
+      const toolsMap = new Map(candidateTools.map((tool: any) => [tool.name, tool]));
+      const conceptsMap = new Map(candidateConcepts.map((concept: any) => [concept.name, concept]));
       
+      const selectedTools = result.selectedTools
+        .map((name: string) => toolsMap.get(name))
+        .filter((tool: any): tool is NonNullable<any> => tool !== undefined && tool !== null);
+      
+      const selectedConcepts = result.selectedConcepts
+        .map((name: string) => conceptsMap.get(name))
+        .filter((concept: any): concept is NonNullable<any> => concept !== undefined && concept !== null);
+      
+      const selectedMethods = selectedTools.filter((t: any) => t && t.type === 'method');
+      const selectedSoftware = selectedTools.filter((t: any) => t && t.type === 'software');
+      
+      console.log('[AI-PIPELINE] AI selected:', selectedMethods.length, 'methods,', selectedSoftware.length, 'software,', selectedConcepts.length, 'concepts');
+
       if (this.auditConfig.enabled) {
-        const confidence = this.calculateSelectionConfidence(result, candidateTools.length);
+        const confidence = this.calculateSelectionConfidence(result, candidateTools.length + candidateConcepts.length);
         
-        this.addAuditEntry(context, 'selection', 'ai-tool-selection',
-          { candidateCount: candidateTools.length, mode, promptLength: prompt.length },
-          { 
-            selectedToolCount: result.selectedTools.length, 
-            selectedConceptCount: result.selectedConcepts.length,
-            reasoning: result.reasoning?.slice(0, 200) + '...',
-            finalToolNames: selectedTools.map(t => t.name),
-            selectionEfficiency: `${toolsToSend.length} → ${result.selectedTools.length}`
+        this.addAuditEntry(
+          context,
+          'selection',
+          'ai-tool-selection',
+          { candidateCount: candidateTools.length, mode },
+          {
+            selectedMethodCount: selectedMethods.length,
+            selectedSoftwareCount: selectedSoftware.length,
+            selectedConceptCount: selectedConcepts.length,
+            reasoning: result.reasoning?.slice(0, 200),
+            methodBalance: `${((selectedMethods.length / (selectedTools.length || 1)) * 100).toFixed(0)}%`
           },
           confidence,
           selectionStart,
-          { aiModel: this.config.model, selectionMethod, promptTokens: estimatedTokens, toolsSent: toolsToSend.length }
+          { aiModel: this.config.model, selectionMethod }
         );
       }
       
-      return {
-        selectedTools,
-        selectedConcepts
-      };
+      return { selectedTools, selectedConcepts };
 
     } catch (error) {
-      console.error('[AI PIPELINE] AI selection failed:', error);
+      console.error('[AI-PIPELINE] AI selection failed:', error);
       
       if (this.auditConfig.enabled) {
-        this.addAuditEntry(context, 'selection', 'ai-tool-selection-error',
+        this.addAuditEntry(
+          context,
+          'selection',
+          'ai-tool-selection-error',
           { candidateCount: candidateTools.length, mode },
           { error: error.message },
           5,
           selectionStart,
-          { aiModel: this.config.model, selectionMethod, tokensSent: estimatedTokens }
+          { aiModel: this.config.model, selectionMethod }
         );
       }
       throw error;
     }
   }
 
+  private createToolData = (tool: any) => ({
+    name: tool.name,
+    type: tool.type,
+    description: tool.description,
+    domains: tool.domains,
+    phases: tool.phases,
+    platforms: tool.platforms || [],
+    tags: tool.tags || [],
+    skillLevel: tool.skillLevel,
+    license: tool.license,
+    accessType: tool.accessType,
+    projectUrl: tool.projectUrl,
+    knowledgebase: tool.knowledgebase,
+    related_concepts: tool.related_concepts || [],
+    related_software: tool.related_software || []
+  });
+
+  private createConceptData = (concept: any) => ({
+    name: concept.name,
+    type: 'concept',
+    description: concept.description,
+    domains: concept.domains,
+    phases: concept.phases,
+    tags: concept.tags || [],
+    skillLevel: concept.skillLevel,
+    related_concepts: concept.related_concepts || [],
+    related_software: concept.related_software || []
+  });
+
   private async delay(ms: number): Promise<void> {
     return new Promise(resolve => setTimeout(resolve, ms));
   }
 
-  private async callMicroTaskAI(prompt: string, context: AnalysisContext, maxTokens: number = 500): Promise<MicroTaskResult> {
+  private async callMicroTaskAI(
+    prompt: string,
+    context: AnalysisContext,
+    maxTokens: number = 500
+  ): Promise<MicroTaskResult> {
     const startTime = Date.now();
     
     let contextPrompt = prompt;
@@ -649,8 +725,6 @@ ${JSON.stringify(conceptsToSend, null, 2)}`;
       
       if (this.estimateTokens(combinedPrompt) <= this.maxPromptTokens) {
         contextPrompt = combinedPrompt;
-      } else {
-        console.warn('[AI PIPELINE] Context too long, using prompt only');
       }
     }
     
@@ -664,9 +738,12 @@ ${JSON.stringify(conceptsToSend, null, 2)}`;
         success: true
       };
       
-      this.addAuditEntry(context, 'micro-task', 'ai-analysis',
+      this.addAuditEntry(
+        context,
+        'micro-task',
+        'ai-analysis',
         { promptLength: contextPrompt.length, maxTokens },
-        { responseLength: response.length, contentPreview: response.slice(0, 100) },
+        { responseLength: response.length },
         response.length > 50 ? 80 : 60,
         startTime,
         { aiModel: this.config.model, contextUsed: context.contextHistory.length > 0 }
@@ -683,12 +760,15 @@ ${JSON.stringify(conceptsToSend, null, 2)}`;
         error: error.message
       };
       
-      this.addAuditEntry(context, 'micro-task', 'ai-analysis-failed',
+      this.addAuditEntry(
+        context,
+        'micro-task',
+        'ai-analysis-failed',
         { promptLength: contextPrompt.length, maxTokens },
         { error: error.message },
         5,
         startTime,
-        { aiModel: this.config.model, contextUsed: context.contextHistory.length > 0 }
+        { aiModel: this.config.model }
       );
       
       return result;
@@ -696,24 +776,21 @@ ${JSON.stringify(conceptsToSend, null, 2)}`;
   }
 
   private calculateRecommendationConfidence(
-    tool: any, 
+    tool: any,
     context: AnalysisContext,
     taskRelevance: number = 70,
     limitations: string[] = []
   ): ConfidenceMetrics {
-    
-    const rawSemanticRelevance = context.embeddingsSimilarities.has(tool.name) ? 
+    const rawSemanticRelevance = context.embeddingsSimilarities.has(tool.name) ?
       context.embeddingsSimilarities.get(tool.name)! * 100 : 50;
     
     let enhancedTaskSuitability = taskRelevance;
     
     if (context.mode === 'workflow') {
-      const toolSelection = context.selectedTools?.find(st => st.tool.name === tool.name);
-      if (toolSelection && tool.phases && tool.phases.includes(toolSelection.phase)) {
+      const toolSelection = context.selectedTools?.find((st: any) => st.tool && st.tool.name === tool.name);
+      if (toolSelection && tool.phases && Array.isArray(tool.phases) && tool.phases.includes(toolSelection.phase)) {
         const phaseBonus = Math.min(15, 100 - taskRelevance);
         enhancedTaskSuitability = Math.min(100, taskRelevance + phaseBonus);
-        
-        console.log(`[CONFIDENCE] Phase bonus for ${tool.name}: ${taskRelevance} -> ${enhancedTaskSuitability} (phase: ${toolSelection.phase})`);
       }
     }
     
@@ -722,44 +799,41 @@ ${JSON.stringify(conceptsToSend, null, 2)}`;
       enhancedTaskSuitability * this.confidenceConfig.suitabilityWeight
     );
 
-    const uncertaintyFactors = this.identifySpecificUncertaintyFactors(tool, context, limitations, overall);
-    const strengthIndicators = this.identifySpecificStrengthIndicators(tool, context, overall);
-
-    console.log(`[CONFIDENCE DEBUG] ${tool.name}:`, {
-      rawSemantic: Math.round(rawSemanticRelevance),
-      rawTaskSuitability: taskRelevance,
-      enhancedTaskSuitability: Math.round(enhancedTaskSuitability),
-      overall: Math.round(overall),
-      mode: context.mode
-    });
+    const uncertaintyFactors = this.identifyUncertaintyFactors(tool, context, limitations, overall);
+    const strengthIndicators = this.identifyStrengthIndicators(tool, context, overall);
 
     return {
       overall: Math.round(overall),
       semanticRelevance: Math.round(rawSemanticRelevance),
-      taskSuitability: Math.round(enhancedTaskSuitability), 
+      taskSuitability: Math.round(enhancedTaskSuitability),
       uncertaintyFactors,
       strengthIndicators
     };
   }
 
-  private identifySpecificUncertaintyFactors(tool: any, context: AnalysisContext, limitations: string[], confidence: number): string[] {
+  private identifyUncertaintyFactors(
+    tool: any,
+    context: AnalysisContext,
+    limitations: string[],
+    confidence: number
+  ): string[] {
     const factors: string[] = [];
     
-    if (limitations && limitations.length > 0) {
+    if (limitations?.length > 0) {
       factors.push(...limitations.slice(0, 2));
     }
     
     const similarity = context.embeddingsSimilarities.get(tool.name) || 0.5;
     if (similarity < 0.7) {
-      factors.push('Geringe semantische Ähnlichkeit zur Anfrage - Tool-Beschreibung passt möglicherweise nicht optimal');
+      factors.push('Geringe semantische Ähnlichkeit zur Anfrage');
     }
     
     if (tool.skillLevel === 'expert' && /schnell|rapid|triage|urgent|sofort/i.test(context.userQuery)) {
-      factors.push('Experten-Tool für zeitkritisches Szenario - Setup und Einarbeitung könnten zu lange dauern');
+      factors.push('Experten-Tool für zeitkritisches Szenario');
     }
     
     if (tool.skillLevel === 'novice' && /komplex|erweitert|tiefgehend|advanced|forensisch/i.test(context.userQuery)) {
-      factors.push('Einsteiger-Tool für komplexe Analyse - könnte funktionale Limitierungen haben');
+      factors.push('Einsteiger-Tool für komplexe Analyse');
     }
     
     if (tool.type === 'software' && !isToolHosted(tool) && tool.accessType === 'download') {
@@ -767,17 +841,17 @@ ${JSON.stringify(conceptsToSend, null, 2)}`;
     }
     
     if (tool.license === 'Proprietary') {
-      factors.push('Kommerzielle Software - Lizenzkosten und rechtliche Beschränkungen zu beachten');
+      factors.push('Kommerzielle Software - Lizenzkosten zu beachten');
     }
     
     if (confidence < 60) {
-      factors.push('Moderate Gesamtbewertung - alternative Ansätze sollten ebenfalls betrachtet werden');
+      factors.push('Moderate Gesamtbewertung - alternative Ansätze empfohlen');
     }
     
     return factors.slice(0, 4);
   }
 
-  private identifySpecificStrengthIndicators(tool: any, context: AnalysisContext, confidence: number): string[] {
+  private identifyStrengthIndicators(tool: any, context: AnalysisContext, confidence: number): string[] {
     const indicators: string[] = [];
     
     const similarity = context.embeddingsSimilarities.get(tool.name) || 0.5;
@@ -790,7 +864,7 @@ ${JSON.stringify(conceptsToSend, null, 2)}`;
     }
     
     if (isToolHosted(tool)) {
-      indicators.push('Sofort verfügbar über gehostete Lösung - kein Setup erforderlich');
+      indicators.push('Sofort verfügbar über gehostete Lösung');
     }
     
     if (tool.skillLevel === 'intermediate' || tool.skillLevel === 'advanced') {
@@ -801,10 +875,11 @@ ${JSON.stringify(conceptsToSend, null, 2)}`;
       indicators.push('Methodischer Ansatz passt zu Ihrer prozeduralen Anfrage');
     }
     
-    return indicators.slice(0, 4); 
+    return indicators.slice(0, 4);
   }
 
   private async analyzeScenario(context: AnalysisContext): Promise<MicroTaskResult> {
+    console.log('[AI-PIPELINE] Starting scenario analysis micro-task');
     const isWorkflow = context.mode === 'workflow';
     const prompt = getPrompt('scenarioAnalysis', isWorkflow, context.userQuery);
 
@@ -824,6 +899,7 @@ ${JSON.stringify(conceptsToSend, null, 2)}`;
   }
 
   private async generateApproach(context: AnalysisContext): Promise<MicroTaskResult> {
+    console.log('[AI-PIPELINE] Starting investigation approach micro-task');
     const isWorkflow = context.mode === 'workflow';
     const prompt = getPrompt('investigationApproach', isWorkflow, context.userQuery);
 
@@ -838,6 +914,7 @@ ${JSON.stringify(conceptsToSend, null, 2)}`;
   }
 
   private async generateCriticalConsiderations(context: AnalysisContext): Promise<MicroTaskResult> {
+    console.log('[AI-PIPELINE] Starting critical considerations micro-task');
     const isWorkflow = context.mode === 'workflow';
     const prompt = getPrompt('criticalConsiderations', isWorkflow, context.userQuery);
 
@@ -852,11 +929,13 @@ ${JSON.stringify(conceptsToSend, null, 2)}`;
   }
 
   private async selectToolsForPhase(context: AnalysisContext, phase: any): Promise<MicroTaskResult> {
-    const phaseTools = context.filteredData.tools.filter((tool: any) => 
-      tool.phases && tool.phases.includes(phase.id)
+    console.log('[AI-PIPELINE] Starting phase tool selection micro-task for:', phase.id);
+    const phaseTools = context.filteredData.tools.filter((tool: any) =>
+      tool && tool.phases && Array.isArray(tool.phases) && tool.phases.includes(phase.id)
     );
 
     if (phaseTools.length === 0) {
+      console.log('[AI-PIPELINE] No tools available for phase:', phase.id);
       return {
         taskType: 'tool-selection',
         content: JSON.stringify([]),
@@ -865,22 +944,32 @@ ${JSON.stringify(conceptsToSend, null, 2)}`;
       };
     }
 
-    const prompt = getPrompt('phaseToolSelection', context.userQuery, phase, phaseTools);
+    const phaseMethods = phaseTools.filter((t: any) => t && t.type === 'method');
+    const phaseSoftware = phaseTools.filter((t: any) => t && t.type === 'software');
 
+    console.log('[AI-PIPELINE] Phase tools available:', phaseMethods.length, 'methods,', phaseSoftware.length, 'software');
+
+    const prompt = getPrompt('phaseToolSelection', context.userQuery, phase, phaseTools);
     const result = await this.callMicroTaskAI(prompt, context, 1000);
     
     if (result.success) {
       const selections = this.safeParseJSON(result.content, []);
       
       if (Array.isArray(selections)) {
-        const validSelections = selections.filter((sel: any) => 
-          sel.toolName && phaseTools.some((tool: any) => tool.name === sel.toolName)
-        );
+        const validSelections = selections.filter((sel: any) => {
+          const matchingTool = phaseTools.find((tool: any) => tool && tool.name === sel.toolName);
+          if (!matchingTool) {
+            console.warn('[AI-PIPELINE] Invalid tool selection for phase:', phase.id, sel.toolName);
+          }
+          return !!matchingTool;
+        });
+        
+        console.log('[AI-PIPELINE] Valid selections for phase:', phase.id, validSelections.length);
         
         validSelections.forEach((sel: any) => {
-          const tool = phaseTools.find((t: any) => t.name === sel.toolName);
+          const tool = phaseTools.find((t: any) => t && t.name === sel.toolName);
           if (tool) {
-            const taskRelevance = typeof sel.taskRelevance === 'number' ? 
+            const taskRelevance = typeof sel.taskRelevance === 'number' ?
               sel.taskRelevance : parseInt(String(sel.taskRelevance)) || 70;
             
             const priority = this.derivePriorityFromScore(taskRelevance);
@@ -889,19 +978,22 @@ ${JSON.stringify(conceptsToSend, null, 2)}`;
           }
         });
         
-        this.addAuditEntry(context, 'micro-task', 'phase-tool-selection',
+        this.addAuditEntry(
+          context,
+          'micro-task',
+          'phase-tool-selection',
           { phase: phase.id, availableTools: phaseTools.length },
-          { 
-            validSelections: validSelections.length, 
-            selectedTools: validSelections.map(s => ({ 
-              name: s.toolName, 
-              taskRelevance: s.taskRelevance, 
-              derivedPriority: this.derivePriorityFromScore(s.taskRelevance) 
+          {
+            validSelections: validSelections.length,
+            selectedTools: validSelections.map((s: any) => ({
+              name: s.toolName,
+              taskRelevance: s.taskRelevance,
+              derivedPriority: this.derivePriorityFromScore(s.taskRelevance)
             }))
           },
           validSelections.length > 0 ? 75 : 30,
           Date.now() - result.processingTimeMs,
-          { phaseName: phase.name, comparativeEvaluation: true, priorityDerived: true }
+          { phaseName: phase.name }
         );
       }
     }
@@ -909,13 +1001,199 @@ ${JSON.stringify(conceptsToSend, null, 2)}`;
     return result;
   }
 
+  private async completeUnderrepresentedPhases(
+    context: AnalysisContext,
+    toolsData: any,
+    originalQuery: string
+  ): Promise<void> {
+    const phases = toolsData.phases || [];
+    const selectedPhases = new Map<string, number>();
+    
+    context.selectedTools?.forEach((st: any) => {
+      const count = selectedPhases.get(st.phase) || 0;
+      selectedPhases.set(st.phase, count + 1);
+    });
+    
+    console.log('[AI-PIPELINE] Phase coverage analysis complete');
+    
+    const phaseQueryTemplates = this.generatePhaseQueryTemplates(phases);
+    
+    const underrepresentedPhases = phases.filter((phase: any) => {
+      const count = selectedPhases.get(phase.id) || 0;
+      return count <= 1;
+    });
+    
+    if (underrepresentedPhases.length === 0) {
+      console.log('[AI-PIPELINE] All phases adequately represented');
+      return;
+    }
+    
+    console.log('[AI-PIPELINE] Completing underrepresented phases:', underrepresentedPhases.map((p: any) => p.id).join(', '));
+    
+    for (const phase of underrepresentedPhases) {
+      await this.completePhaseWithSemanticSearch(context, phase, phaseQueryTemplates, toolsData, originalQuery);
+      await this.delay(this.microTaskDelay);
+    }
+  }
+
+  private async completePhaseWithSemanticSearch(
+    context: AnalysisContext,
+    phase: any,
+    phaseQueryTemplates: Record<string, string>,
+    toolsData: any,
+    originalQuery: string
+  ): Promise<void> {
+    const phaseStart = Date.now();
+    const phaseQuery = phaseQueryTemplates[phase.id] || `forensic ${phase.name.toLowerCase()} tools methods`;
+    
+    console.log('[AI-PIPELINE] Starting enhanced phase completion micro-task for:', phase.id);
+    
+    try {
+      const phaseResults = await embeddingsService.findSimilar(phaseQuery, 20, 0.2);
+      
+      if (phaseResults.length === 0) {
+        console.log('[AI-PIPELINE] No semantic results for phase:', phase.id);
+        return;
+      }
+      
+      const toolsMap = new Map(toolsData.tools.map((tool: any) => [tool.name, tool]));
+      const conceptsMap = new Map(toolsData.concepts.map((concept: any) => [concept.name, concept]));
+      
+      const phaseTools = phaseResults
+        .filter((result: any) => result.type === 'tool')
+        .map((result: any) => toolsMap.get(result.name))
+        .filter((tool: any): tool is NonNullable<any> =>
+          tool !== undefined &&
+          tool !== null &&
+          tool.phases &&
+          Array.isArray(tool.phases) &&
+          tool.phases.includes(phase.id) &&
+          !context.seenToolNames.has(tool.name)
+        )
+        .slice(0, 5);
+      
+      const phaseConcepts = phaseResults
+        .filter((result: any) => result.type === 'concept')
+        .map((result: any) => conceptsMap.get(result.name))
+        .filter((concept: any): concept is NonNullable<any> => concept !== undefined && concept !== null)
+        .slice(0, 2);
+      
+      if (phaseTools.length === 0) {
+        console.log('[AI-PIPELINE] No suitable tools for phase completion:', phase.id);
+        return;
+      }
+      
+      // Step 1: AI selection of tools for completion
+      const selectionPrompt = AI_PROMPTS.generatePhaseCompletionPrompt(originalQuery, phase, phaseTools, phaseConcepts);
+      const selectionResult = await this.callMicroTaskAI(selectionPrompt, context, 800);
+      
+      if (!selectionResult.success) {
+        console.error('[AI-PIPELINE] Phase completion selection failed for:', phase.id);
+        return;
+      }
+      
+      const selection = this.safeParseJSON(selectionResult.content, { 
+        selectedTools: [], 
+        selectedConcepts: [],
+        completionReasoning: ''
+      });
+      
+      const validTools = selection.selectedTools
+        .map((name: string) => phaseTools.find((t: any) => t && t.name === name))
+        .filter((tool: any): tool is NonNullable<any> => tool !== undefined && tool !== null)
+        .slice(0, 2);
+      
+      if (validTools.length === 0) {
+        console.log('[AI-PIPELINE] No valid tools selected for phase completion:', phase.id);
+        return;
+      }
+      
+      // Step 2: Generate detailed reasoning for each selected tool
+      for (const tool of validTools) {
+        console.log('[AI-PIPELINE] Generating reasoning for phase completion tool:', tool.name);
+        
+        const reasoningPrompt = getPrompt(
+          'phaseCompletionReasoning',
+          originalQuery,
+          phase,
+          tool.name,
+          tool,
+          selection.completionReasoning || 'Nachergänzung zur Vervollständigung der Phasenabdeckung'
+        );
+        
+        const reasoningResult = await this.callMicroTaskAI(reasoningPrompt, context, 400);
+        
+        let detailedJustification: string;
+        if (reasoningResult.success) {
+          detailedJustification = reasoningResult.content.trim();
+        } else {
+          detailedJustification = `Nachträglich hinzugefügt zur Vervollständigung der ${phase.name}-Phase. Die ursprüngliche KI-Auswahl war zu spezifisch und hat wichtige Tools für diese Phase übersehen.`;
+        }
+        
+        this.addToolToSelection(
+          context,
+          tool,
+          phase.id,
+          'medium',
+          detailedJustification,
+          75,
+          ['Nachträgliche Ergänzung via semantische Phasensuche']
+        );
+        
+        console.log('[AI-PIPELINE] Added phase completion tool with reasoning:', tool.name);
+      }
+      
+      this.addAuditEntry(
+        context,
+        'validation',
+        'phase-completion',
+        { 
+          phase: phase.id, 
+          phaseQuery, 
+          candidatesFound: phaseTools.length,
+          selectionReasoning: selection.completionReasoning
+        },
+        { 
+          toolsAdded: validTools.length, 
+          addedTools: validTools.map((t: any) => ({
+            name: t.name,
+            type: t.type,
+            reasoning: 'Generated via micro-task'
+          }))
+        },
+        validTools.length > 0 ? 80 : 40,
+        phaseStart,
+        { 
+          phaseCompletion: true, 
+          semanticSearch: true,
+          microTaskReasoning: true,
+          contextualExplanation: true
+        }
+      );
+      
+    } catch (error) {
+      console.error('[AI-PIPELINE] Enhanced phase completion failed for:', phase.id, error);
+      
+      this.addAuditEntry(
+        context,
+        'validation',
+        'phase-completion-failed',
+        { phase: phase.id, phaseQuery },
+        { error: error.message },
+        10,
+        phaseStart,
+        { phaseCompletion: true, failed: true }
+      );
+    }
+  }
+
   private async evaluateSpecificTool(context: AnalysisContext, tool: any, rank: number): Promise<MicroTaskResult> {
-    const existingSelection = context.selectedTools?.find(st => st.tool.name === tool.name);
+    console.log('[AI-PIPELINE] Starting tool evaluation micro-task for:', tool.name);
+    const existingSelection = context.selectedTools?.find((st: any) => st.tool && st.tool.name === tool.name);
     const taskRelevance = existingSelection?.taskRelevance || 70;
     const priority = this.derivePriorityFromScore(taskRelevance);
     
     const prompt = getPrompt('toolEvaluation', context.userQuery, tool, rank, taskRelevance);
-
     const result = await this.callMicroTaskAI(prompt, context, 1000);
     
     if (result.success) {
@@ -923,7 +1201,7 @@ ${JSON.stringify(conceptsToSend, null, 2)}`;
         detailed_explanation: 'Evaluation failed',
         implementation_approach: '',
         pros: [],
-        limitations: [], 
+        limitations: [],
         alternatives: ''
       });
       
@@ -934,21 +1212,23 @@ ${JSON.stringify(conceptsToSend, null, 2)}`;
           rank,
           task_relevance: taskRelevance
         }
-      }, 'evaluation', priority, evaluation.detailed_explanation, 
-      taskRelevance, evaluation.limitations); 
+      }, 'evaluation', priority, evaluation.detailed_explanation,
+      taskRelevance, evaluation.limitations);
       
-      this.addAuditEntry(context, 'micro-task', 'tool-evaluation',
-        { toolName: tool.name, rank, existingTaskRelevance: taskRelevance, derivedPriority: priority },
-        { 
+      this.addAuditEntry(
+        context,
+        'micro-task',
+        'tool-evaluation',
+        { toolName: tool.name, rank, existingTaskRelevance: taskRelevance },
+        {
           hasExplanation: !!evaluation.detailed_explanation,
           hasImplementationApproach: !!evaluation.implementation_approach,
           prosCount: evaluation.pros?.length || 0,
-          limitationsCount: evaluation.limitations?.length || 0,
-          hasLimitations: Array.isArray(evaluation.limitations) && evaluation.limitations.length > 0
+          limitationsCount: evaluation.limitations?.length || 0
         },
         70,
         Date.now() - result.processingTimeMs,
-        { toolType: tool.type, explanationOnly: true, priorityDerived: true, limitationsExtracted: true }
+        { toolType: tool.type }
       );
     }
     
@@ -956,6 +1236,7 @@ ${JSON.stringify(conceptsToSend, null, 2)}`;
   }
 
   private async selectBackgroundKnowledge(context: AnalysisContext): Promise<MicroTaskResult> {
+    console.log('[AI-PIPELINE] Starting background knowledge selection micro-task');
     const availableConcepts = context.filteredData.concepts;
     
     if (availableConcepts.length === 0) {
@@ -967,23 +1248,25 @@ ${JSON.stringify(conceptsToSend, null, 2)}`;
       };
     }
 
-    const selectedToolNames = context.selectedTools?.map(st => st.tool.name) || [];
+    const selectedToolNames = context.selectedTools?.map((st: any) => st.tool && st.tool.name).filter(Boolean) || [];
     const prompt = getPrompt('backgroundKnowledgeSelection', context.userQuery, context.mode, selectedToolNames, availableConcepts);
-
     const result = await this.callMicroTaskAI(prompt, context, 700);
     
     if (result.success) {
       const selections = this.safeParseJSON(result.content, []);
       
       if (Array.isArray(selections)) {
-        context.backgroundKnowledge = selections.filter((sel: any) => 
+        context.backgroundKnowledge = selections.filter((sel: any) =>
           sel.conceptName && availableConcepts.some((concept: any) => concept.name === sel.conceptName)
         ).map((sel: any) => ({
           concept: availableConcepts.find((c: any) => c.name === sel.conceptName),
           relevance: sel.relevance
         }));
         
-        this.addAuditEntry(context, 'micro-task', 'background-knowledge-selection',
+        this.addAuditEntry(
+          context,
+          'micro-task',
+          'background-knowledge-selection',
           { availableConcepts: availableConcepts.length },
           { selectedConcepts: context.backgroundKnowledge?.length || 0 },
           context.backgroundKnowledge && context.backgroundKnowledge.length > 0 ? 75 : 40,
@@ -997,9 +1280,10 @@ ${JSON.stringify(conceptsToSend, null, 2)}`;
   }
 
   private async generateFinalRecommendations(context: AnalysisContext): Promise<MicroTaskResult> {
-    const selectedToolNames = context.selectedTools?.map(st => st.tool.name) || [];
+    console.log('[AI-PIPELINE] Starting final recommendations micro-task');
+    const selectedToolNames = context.selectedTools?.map((st: any) => st.tool && st.tool.name).filter(Boolean) || [];
     const prompt = getPrompt('finalRecommendations', context.mode === 'workflow', context.userQuery, selectedToolNames);
-
+    
     const result = await this.callMicroTaskAI(prompt, context, 350);
     return result;
   }
@@ -1015,9 +1299,6 @@ ${JSON.stringify(conceptsToSend, null, 2)}`;
     
     if (apiKey) {
       headers['Authorization'] = `Bearer ${apiKey}`;
-      console.log('[AI PIPELINE] Using API key authentication');
-    } else {
-      console.log('[AI PIPELINE] No API key - making request without authentication');
     }
     
     const requestBody = {
@@ -1036,7 +1317,7 @@ ${JSON.stringify(conceptsToSend, null, 2)}`;
 
       if (!response.ok) {
         const errorText = await response.text();
-        console.error(`[AI PIPELINE] AI API Error ${response.status}:`, errorText);
+        console.error('[AI-PIPELINE] AI API Error:', response.status, errorText);
         throw new Error(`AI API error: ${response.status} - ${errorText}`);
       }
 
@@ -1044,14 +1325,14 @@ ${JSON.stringify(conceptsToSend, null, 2)}`;
       const content = data.choices?.[0]?.message?.content;
       
       if (!content) {
-        console.error('[AI PIPELINE] No response content:', data);
+        console.error('[AI-PIPELINE] No response content from AI model');
         throw new Error('No response from AI model');
       }
 
       return content;
       
     } catch (error) {
-      console.error('[AI PIPELINE] AI service call failed:', error.message);
+      console.error('[AI-PIPELINE] AI service call failed:', error.message);
       throw error;
     }
   }
@@ -1062,14 +1343,35 @@ ${JSON.stringify(conceptsToSend, null, 2)}`;
     return 'low';
   }
 
+  private async performAISelection(
+    filteredData: any,
+    userQuery: string,
+    mode: string,
+    context: AnalysisContext
+  ): Promise<{ tools: any[], concepts: any[] }> {
+    const result = await this.aiSelectionWithFullData(
+      userQuery,
+      filteredData.tools,
+      filteredData.concepts,
+      mode,
+      embeddingsService.isEnabled() ? 'embeddings_candidates' : 'full_dataset',
+      context
+    );
+    
+    console.log('[AI-PIPELINE] AI selection complete:', result.selectedTools.length, 'tools,', result.selectedConcepts.length, 'concepts');
+    
+    return {
+      tools: result.selectedTools,
+      concepts: result.selectedConcepts
+    };
+  }
+
   async processQuery(userQuery: string, mode: string): Promise<AnalysisResult> {
     const startTime = Date.now();
     let completeTasks = 0;
     let failedTasks = 0;
     
-    this.tempAuditEntries = [];
-    
-    console.log(`[AI PIPELINE] Starting ${mode} query processing with enhanced confidence scoring`);
+    console.log('[AI-PIPELINE] Starting', mode, 'query processing');
 
     try {
       const toolsData = await getCompressedToolsDataForAI();
@@ -1083,22 +1385,34 @@ ${JSON.stringify(conceptsToSend, null, 2)}`;
         currentContextLength: 0,
         seenToolNames: new Set<string>(),
         auditTrail: [],
-        embeddingsSimilarities: new Map<string, number>()
+        embeddingsSimilarities: new Map<string, number>(),
+        aiSelectedTools: [],
+        aiSelectedConcepts: []
       };
 
       const filteredData = await this.getIntelligentCandidates(userQuery, toolsData, mode, context);
-      context.filteredData = filteredData;
+      const aiSelection = await this.performAISelection(filteredData, userQuery, mode, context);
       
-      this.mergeTemporaryAuditEntries(context);
+      context.aiSelectedTools = aiSelection.tools;
+      context.aiSelectedConcepts = aiSelection.concepts;
+      
+      context.filteredData = {
+        tools: aiSelection.tools,
+        concepts: aiSelection.concepts,
+        domains: filteredData.domains,
+        phases: filteredData.phases,
+        'domain-agnostic-software': filteredData['domain-agnostic-software']
+      };
 
-      console.log(`[AI PIPELINE] Starting micro-tasks with ${filteredData.tools.length} tools visible`);
-
-      this.addAuditEntry(context, 'initialization', 'pipeline-start',
+      this.addAuditEntry(
+        context,
+        'initialization',
+        'pipeline-start',
         { userQuery, mode, toolsDataLoaded: !!toolsData },
         { candidateTools: filteredData.tools.length, candidateConcepts: filteredData.concepts.length },
-        90, 
+        90,
         startTime,
-        { auditEnabled: this.auditConfig.enabled, confidenceScoringEnabled: true }
+        { auditEnabled: this.auditConfig.enabled }
       );
    
       const analysisResult = await this.analyzeScenario(context);
@@ -1115,11 +1429,15 @@ ${JSON.stringify(conceptsToSend, null, 2)}`;
 
       if (mode === 'workflow') {
         const phases = toolsData.phases || [];
+        
         for (const phase of phases) {
           const toolSelectionResult = await this.selectToolsForPhase(context, phase);
           if (toolSelectionResult.success) completeTasks++; else failedTasks++;
           await this.delay(this.microTaskDelay);
         }
+        
+        await this.completeUnderrepresentedPhases(context, toolsData, userQuery);
+        
       } else {
         const topTools = filteredData.tools.slice(0, 3);
         for (let i = 0; i < topTools.length; i++) {
@@ -1138,28 +1456,28 @@ ${JSON.stringify(conceptsToSend, null, 2)}`;
 
       const recommendation = this.buildRecommendation(context, mode, finalResult.content);
 
-      this.addAuditEntry(context, 'completion', 'pipeline-end',
+      this.addAuditEntry(
+        context,
+        'completion',
+        'pipeline-end',
         { completedTasks: completeTasks, failedTasks },
         { finalRecommendation: !!recommendation, auditEntriesGenerated: context.auditTrail.length },
         completeTasks > failedTasks ? 85 : 60,
         startTime,
-        { totalProcessingTimeMs: Date.now() - startTime, confidenceScoresGenerated: context.selectedTools?.length || 0 }
+        { totalProcessingTimeMs: Date.now() - startTime }
       );
 
       const processingStats = {
         embeddingsUsed: embeddingsService.isEnabled(),
         candidatesFromEmbeddings: filteredData.tools.length,
-        finalSelectedItems: (context.selectedTools?.length || 0) + 
-                           (context.backgroundKnowledge?.length || 0),
+        finalSelectedItems: (context.selectedTools?.length || 0) + (context.backgroundKnowledge?.length || 0),
         processingTimeMs: Date.now() - startTime,
         microTasksCompleted: completeTasks,
         microTasksFailed: failedTasks,
         contextContinuityUsed: true
       };
 
-      console.log(`[AI PIPELINE] Completed: ${completeTasks} tasks, Failed: ${failedTasks} tasks`);
-      console.log(`[AI PIPELINE] Enhanced confidence scores generated: ${context.selectedTools?.length || 0}`);
-      console.log(`[AI PIPELINE] Audit trail entries: ${context.auditTrail.length}`);
+      console.log('[AI-PIPELINE] Processing complete. Tasks completed:', completeTasks, 'failed:', failedTasks);
 
       return {
         recommendation: {
@@ -1170,10 +1488,7 @@ ${JSON.stringify(conceptsToSend, null, 2)}`;
       };
 
     } catch (error) {
-      console.error('[AI PIPELINE] Processing failed:', error);
-      
-      this.tempAuditEntries = [];
-      
+      console.error('[AI-PIPELINE] Processing failed:', error);
       throw error;
     }
   }
@@ -1181,19 +1496,38 @@ ${JSON.stringify(conceptsToSend, null, 2)}`;
   private buildRecommendation(context: AnalysisContext, mode: string, finalContent: string): any {
     const isWorkflow = mode === 'workflow';
     
+    console.log('[AI-PIPELINE] Building recommendation for', mode, 'mode with', context.selectedTools?.length || 0, 'tools');
+    
+    if (context.selectedTools && context.selectedTools.length > 0) {
+      const methods = context.selectedTools.filter((st: any) => st.tool && st.tool.type === 'method');
+      const software = context.selectedTools.filter((st: any) => st.tool && st.tool.type === 'software');
+      
+      console.log('[AI-PIPELINE] Final selection breakdown:', methods.length, 'methods,', software.length, 'software');
+      console.log('[AI-PIPELINE] Method names:', methods.map((m: any) => m.tool.name).join(', '));
+      console.log('[AI-PIPELINE] Software names:', software.map((s: any) => s.tool.name).join(', '));
+      
+      context.selectedTools.forEach((st: any, index: number) => {
+        console.log('[AI-PIPELINE] Selected tool', index + 1, ':', st.tool.name, '(' + st.tool.type + ') - Phase:', st.phase, ', Priority:', st.priority);
+      });
+    } else {
+      console.warn('[AI-PIPELINE] WARNING: No tools in selectedTools array!');
+    }
+    
     const base = {
       [isWorkflow ? 'scenario_analysis' : 'problem_analysis']: 
         isWorkflow ? context.scenarioAnalysis : context.problemAnalysis,
       investigation_approach: context.investigationApproach,
       critical_considerations: context.criticalConsiderations,
-      background_knowledge: context.backgroundKnowledge?.map(bk => ({
+      background_knowledge: context.backgroundKnowledge?.map((bk: any) => ({
         concept_name: bk.concept.name,
         relevance: bk.relevance
       })) || []
     };
 
+    const processedAuditTrail = this.auditConfig.enabled && context.auditTrail ? context.auditTrail : [];
+
     if (isWorkflow) {
-      const recommendedToolsWithConfidence = context.selectedTools?.map(st => {
+      const recommendedToolsWithConfidence = context.selectedTools?.map((st: any) => {
         const confidence = this.calculateRecommendationConfidence(
           st.tool,
           context,
@@ -1201,9 +1535,12 @@ ${JSON.stringify(conceptsToSend, null, 2)}`;
           st.limitations || []
         );
         
-        this.addAuditEntry(context, 'validation', 'confidence-scoring',
-          { toolName: st.tool.name, phase: st.phase },
-          { 
+        this.addAuditEntry(
+          context,
+          'validation',
+          'confidence-scoring',
+          { toolName: st.tool.name, toolType: st.tool.type, phase: st.phase },
+          {
             overall: confidence.overall,
             components: {
               semantic: confidence.semanticRelevance,
@@ -1217,6 +1554,7 @@ ${JSON.stringify(conceptsToSend, null, 2)}`;
 
         return {
           name: st.tool.name,
+          type: st.tool.type,
           phase: st.phase,
           priority: st.priority,
           justification: st.justification || `Empfohlen für ${st.phase}`,
@@ -1229,31 +1567,35 @@ ${JSON.stringify(conceptsToSend, null, 2)}`;
       return {
         ...base,
         recommended_tools: recommendedToolsWithConfidence,
-        workflow_suggestion: finalContent
+        workflow_suggestion: finalContent,
+        auditTrail: processedAuditTrail
       };
     } else {
-      const recommendedToolsWithConfidence = context.selectedTools?.map(st => {
+      const recommendedToolsWithConfidence = context.selectedTools?.map((st: any) => {
         const confidence = this.calculateRecommendationConfidence(
           st.tool,
           context,
           st.taskRelevance || 70,
-          st.limitations || [] 
+          st.limitations || []
         );
         
-        this.addAuditEntry(context, 'validation', 'confidence-scoring',
-          { toolName: st.tool.name, rank: st.tool.evaluation?.rank || 1 },
-          { 
+        this.addAuditEntry(
+          context,
+          'validation',
+          'confidence-scoring',
+          { toolName: st.tool.name, toolType: st.tool.type, rank: st.tool.evaluation?.rank || 1 },
+          {
             overall: confidence.overall,
-            suitabilityAlignment: st.priority === 'high' && confidence.overall >= this.confidenceConfig.highThreshold,
-            limitationsUsed: st.limitations?.length || 0
+            suitabilityAlignment: st.priority === 'high' && confidence.overall >= this.confidenceConfig.highThreshold
           },
           confidence.overall,
           Date.now(),
-          { strengthCount: confidence.strengthIndicators.length, limitationsCount: confidence.uncertaintyFactors.length }
+          { strengthCount: confidence.strengthIndicators.length }
         );
 
         return {
           name: st.tool.name,
+          type: st.tool.type,
           rank: st.tool.evaluation?.rank || 1,
           suitability_score: st.priority,
           detailed_explanation: st.tool.evaluation?.detailed_explanation || '',
@@ -1270,7 +1612,8 @@ ${JSON.stringify(conceptsToSend, null, 2)}`;
       return {
         ...base,
         recommended_tools: recommendedToolsWithConfidence,
-        additional_considerations: finalContent
+        additional_considerations: finalContent,
+        auditTrail: processedAuditTrail
       };
     }
   }
diff --git a/src/utils/auditService.ts b/src/utils/auditService.ts
new file mode 100644
index 0000000..b2ad986
--- /dev/null
+++ b/src/utils/auditService.ts
@@ -0,0 +1,411 @@
+// src/utils/auditService.ts 
+import 'dotenv/config';
+
+function env(key: string, fallback: string | undefined = undefined): string | undefined {
+  if (typeof process !== 'undefined' && process.env?.[key] !== undefined) {
+    return process.env[key];
+  }
+  if (typeof import.meta !== 'undefined' && (import.meta as any).env?.[key] !== undefined) {
+    return (import.meta as any).env[key];
+  }
+  return fallback;
+}
+
+interface AuditEntry {
+  timestamp: number;
+  phase: string;
+  action: string;
+  input: any;
+  output: any;
+  confidence: number;
+  processingTimeMs: number;
+  metadata: Record<string, any>;
+}
+
+interface AuditConfig {
+  enabled: boolean;
+  detailLevel: 'minimal' | 'standard' | 'verbose';
+  retentionHours: number;
+  maxEntries: number;
+}
+
+interface CompressedAuditEntry {
+  timestamp: number;
+  phase: string;
+  action: string;
+  inputSummary: string;
+  outputSummary: string;
+  confidence: number;
+  processingTimeMs: number;
+  metadata: Record<string, any>;
+}
+
+interface ProcessedAuditTrail {
+  totalTime: number;
+  avgConfidence: number;
+  stepCount: number;
+  highConfidenceSteps: number;
+  lowConfidenceSteps: number;
+  phases: Array<{
+    name: string;
+    icon: string;
+    displayName: string;
+    avgConfidence: number;
+    totalTime: number;
+    entries: CompressedAuditEntry[];
+  }>;
+  summary: {
+    analysisQuality: 'excellent' | 'good' | 'fair' | 'poor';
+    keyInsights: string[];
+    potentialIssues: string[];
+  };
+}
+
+class AuditService {
+  private config: AuditConfig;
+  private tempEntries: AuditEntry[] = [];
+  
+  private readonly phaseConfig = {
+    'initialization': { icon: '🚀', displayName: 'Initialisierung' },
+    'retrieval': { icon: '🔍', displayName: 'Datensuche' },
+    'selection': { icon: '🎯', displayName: 'Tool-Auswahl' },
+    'micro-task': { icon: '⚡', displayName: 'Detail-Analyse' },
+    'validation': { icon: '✓', displayName: 'Validierung' },
+    'completion': { icon: '✅', displayName: 'Finalisierung' }
+  };
+
+  private readonly actionTranslations = {
+    'pipeline-start': 'Analyse gestartet',
+    'embeddings-search': 'Ähnliche Tools gesucht', 
+    'ai-tool-selection': 'Tools automatisch ausgewählt',
+    'ai-analysis': 'KI-Analyse durchgeführt',
+    'phase-tool-selection': 'Phasen-Tools evaluiert',
+    'tool-evaluation': 'Tool-Bewertung erstellt',
+    'background-knowledge-selection': 'Hintergrundwissen ausgewählt',
+    'confidence-scoring': 'Vertrauenswertung berechnet',
+    'pipeline-end': 'Analyse abgeschlossen'
+  };
+
+  constructor() {
+    this.config = this.loadConfig();
+  }
+
+  private loadConfig(): AuditConfig {
+    const enabledFlag = env('FORENSIC_AUDIT_ENABLED', 'false');
+    const detailLevel = env('FORENSIC_AUDIT_DETAIL_LEVEL', 'standard') as 'minimal' | 'standard' | 'verbose';
+    const retentionHours = parseInt(env('FORENSIC_AUDIT_RETENTION_HOURS', '72') || '72', 10);
+    const maxEntries = parseInt(env('FORENSIC_AUDIT_MAX_ENTRIES', '50') || '50', 10);
+
+    console.log('[AUDIT SERVICE] Configuration loaded:', {
+      enabled: enabledFlag === 'true',
+      detailLevel,
+      retentionHours,
+      maxEntries,
+      context: typeof process !== 'undefined' ? 'server' : 'client'
+    });
+
+    return {
+      enabled: enabledFlag === 'true',
+      detailLevel,
+      retentionHours,
+      maxEntries
+    };
+  }
+
+  getDebugInfo(): {
+    config: AuditConfig;
+    environment: Record<string, any>;
+    context: string;
+  } {
+    const context = typeof process !== 'undefined' ? 'server' : 'client';
+    
+    return {
+      config: this.config,
+      environment: {
+        FORENSIC_AUDIT_ENABLED: env('FORENSIC_AUDIT_ENABLED'),
+        FORENSIC_AUDIT_DETAIL_LEVEL: env('FORENSIC_AUDIT_DETAIL_LEVEL'),
+        FORENSIC_AUDIT_RETENTION_HOURS: env('FORENSIC_AUDIT_RETENTION_HOURS'),
+        FORENSIC_AUDIT_MAX_ENTRIES: env('FORENSIC_AUDIT_MAX_ENTRIES'),
+        processEnvKeys: typeof process !== 'undefined' ? Object.keys(process.env).filter(k => k.includes('AUDIT')) : [],
+        importMetaEnvAvailable: typeof import.meta !== 'undefined' && !!(import.meta as any).env
+      },
+      context
+    };
+  }
+
+  addEntry(
+    phase: string,
+    action: string,
+    input: any,
+    output: any,
+    confidence: number,
+    startTime: number,
+    metadata: Record<string, any> = {}
+  ): void {
+    if (!this.config.enabled) return;
+
+    const entry: AuditEntry = {
+      timestamp: Date.now(),
+      phase,
+      action,
+      input: this.compressData(input),
+      output: this.compressData(output),
+      confidence: Math.round(confidence),
+      processingTimeMs: Date.now() - startTime,
+      metadata
+    };
+
+    this.tempEntries.push(entry);
+    console.log(`[AUDIT] ${phase}/${action}: ${confidence}% confidence, ${entry.processingTimeMs}ms`);
+  }
+
+
+  mergeAndClear(auditTrail: AuditEntry[]): void {
+    if (!this.config.enabled || this.tempEntries.length === 0) return;
+
+    auditTrail.unshift(...this.tempEntries);
+    const entryCount = this.tempEntries.length;
+    this.tempEntries = [];
+    
+    console.log(`[AUDIT] Merged ${entryCount} entries into audit trail`);
+  }
+
+
+  processAuditTrail(rawAuditTrail: AuditEntry[]): ProcessedAuditTrail | null {
+    if (!this.config.enabled) {
+        console.log('[AUDIT] Service disabled, returning null');
+        return null;
+    }
+    
+    if (!rawAuditTrail || !Array.isArray(rawAuditTrail) || rawAuditTrail.length === 0) {
+        console.log('[AUDIT] No audit trail data provided');
+        return null;
+    }
+
+    try {
+        console.log('[AUDIT] Processing', rawAuditTrail.length, 'audit entries');
+
+        const totalTime = rawAuditTrail.reduce((sum, entry) => sum + (entry.processingTimeMs || 0), 0);
+        const validConfidenceEntries = rawAuditTrail.filter(entry => typeof entry.confidence === 'number');
+        const avgConfidence = validConfidenceEntries.length > 0 
+        ? Math.round(validConfidenceEntries.reduce((sum, entry) => sum + entry.confidence, 0) / validConfidenceEntries.length)
+        : 0;
+        
+        const highConfidenceSteps = rawAuditTrail.filter(entry => (entry.confidence || 0) >= 80).length;
+        const lowConfidenceSteps = rawAuditTrail.filter(entry => (entry.confidence || 0) < 60).length;
+
+        const groupedEntries = rawAuditTrail.reduce((groups, entry) => {
+        const phase = entry.phase || 'unknown';
+        if (!groups[phase]) groups[phase] = [];
+        groups[phase].push(entry);
+        return groups;
+        }, {} as Record<string, AuditEntry[]>);
+
+        const phases = Object.entries(groupedEntries).map(([phase, entries]) => {
+        const phaseConfig = this.phaseConfig[phase] || { icon: '📋', displayName: phase };
+        const validEntries = entries.filter(entry => entry && typeof entry === 'object');
+        
+        const phaseAvgConfidence = validEntries.length > 0 
+            ? Math.round(validEntries.reduce((sum, entry) => sum + (entry.confidence || 0), 0) / validEntries.length)
+            : 0;
+        
+        const phaseTotalTime = validEntries.reduce((sum, entry) => sum + (entry.processingTimeMs || 0), 0);
+
+        return {
+            name: phase,
+            icon: phaseConfig.icon,
+            displayName: phaseConfig.displayName,
+            avgConfidence: phaseAvgConfidence,
+            totalTime: phaseTotalTime,
+            entries: validEntries
+  .map(e => this.compressEntry(e))
+  .filter((e): e is CompressedAuditEntry => e !== null)
+        };
+        }).filter(phase => phase.entries.length > 0);
+
+        const summary = this.generateSummary(rawAuditTrail, avgConfidence, lowConfidenceSteps);
+
+        const result: ProcessedAuditTrail = {
+        totalTime,
+        avgConfidence,
+        stepCount: rawAuditTrail.length,
+        highConfidenceSteps,
+        lowConfidenceSteps,
+        phases,
+        summary
+        };
+
+        console.log('[AUDIT] Successfully processed audit trail:', result);
+        return result;
+        
+    } catch (error) {
+        console.error('[AUDIT] Error processing audit trail:', error);
+        return null;
+    }
+  }
+
+  private compressEntry(entry: AuditEntry): CompressedAuditEntry | null {
+    if (!entry || typeof entry !== 'object') {
+        console.warn('[AUDIT] Invalid audit entry:', entry);
+        return null;
+    }
+
+    try {
+        return {
+        timestamp: entry.timestamp || Date.now(),
+        phase: entry.phase || 'unknown',
+        action: entry.action || 'unknown',
+        inputSummary: this.summarizeData(entry.input),
+        outputSummary: this.summarizeData(entry.output),
+        confidence: entry.confidence || 0,
+        processingTimeMs: entry.processingTimeMs || 0,
+        metadata: entry.metadata || {}
+        };
+    } catch (error) {
+        console.error('[AUDIT] Error compressing entry:', error);
+        return null;
+    }
+  }
+
+  private compressData(data: any): any {
+    if (this.config.detailLevel === 'verbose') {
+      return data; 
+    } else if (this.config.detailLevel === 'standard') {
+      return this.summarizeForStorage(data);
+    } else {
+      return this.minimalSummary(data);
+    }
+  }
+
+  private summarizeData(data: any): string {
+    if (data === null || data === undefined) return 'null';
+    if (typeof data === 'string') {
+      return data.length > 100 ? data.slice(0, 100) + '...' : data;
+    }
+    if (typeof data === 'number' || typeof data === 'boolean') {
+      return data.toString();
+    }
+    if (Array.isArray(data)) {
+      if (data.length === 0) return '[]';
+      if (data.length <= 3) return JSON.stringify(data);
+      return `[${data.slice(0, 3).map(i => typeof i === 'string' ? i : JSON.stringify(i)).join(', ')}, ...+${data.length - 3}]`;
+    }
+    if (typeof data === 'object') {
+      const keys = Object.keys(data);
+      if (keys.length === 0) return '{}';
+      if (keys.length <= 3) {
+        return '{' + keys.map(k => `${k}: ${typeof data[k] === 'string' ? data[k].slice(0, 20) + (data[k].length > 20 ? '...' : '') : JSON.stringify(data[k])}`).join(', ') + '}';
+      }
+      return `{${keys.slice(0, 3).join(', ')}, ...+${keys.length - 3} keys}`;
+    }
+    return String(data);
+  }
+
+  private summarizeForStorage(data: any): any {
+    if (typeof data === 'string' && data.length > 500) {
+      return data.slice(0, 500) + '...[truncated]';
+    }
+    if (Array.isArray(data) && data.length > 10) {
+      return [...data.slice(0, 10), `...[${data.length - 10} more items]`];
+    }
+    return data;
+  }
+
+  private minimalSummary(data: any): any {
+    if (typeof data === 'string' && data.length > 100) {
+      return data.slice(0, 100) + '...[truncated]';
+    }
+    if (Array.isArray(data) && data.length > 3) {
+      return [...data.slice(0, 3), `...[${data.length - 3} more items]`];
+    }
+    return data;
+  }
+
+  private generateSummary(entries: AuditEntry[], avgConfidence: number, lowConfidenceSteps: number): {
+    analysisQuality: 'excellent' | 'good' | 'fair' | 'poor';
+    keyInsights: string[];
+    potentialIssues: string[];
+  } {
+    let analysisQuality: 'excellent' | 'good' | 'fair' | 'poor';
+    if (avgConfidence >= 85 && lowConfidenceSteps === 0) {
+      analysisQuality = 'excellent';
+    } else if (avgConfidence >= 70 && lowConfidenceSteps <= 1) {
+      analysisQuality = 'good';
+    } else if (avgConfidence >= 60 && lowConfidenceSteps <= 3) {
+      analysisQuality = 'fair';
+    } else {
+      analysisQuality = 'poor';
+    }
+
+    const keyInsights: string[] = [];
+    const embeddingsUsed = entries.some(e => e.action === 'embeddings-search');
+    if (embeddingsUsed) {
+      keyInsights.push('Semantische Suche wurde erfolgreich eingesetzt');
+    }
+
+    const toolSelectionEntries = entries.filter(e => e.action === 'ai-tool-selection');
+    if (toolSelectionEntries.length > 0) {
+      const avgSelectionConfidence = toolSelectionEntries.reduce((sum, e) => sum + e.confidence, 0) / toolSelectionEntries.length;
+      if (avgSelectionConfidence >= 80) {
+        keyInsights.push('Hohe Konfidenz bei der Tool-Auswahl');
+      }
+    }
+
+    const potentialIssues: string[] = [];
+    if (lowConfidenceSteps > 2) {
+      potentialIssues.push(`${lowConfidenceSteps} Analyseschritte mit niedriger Konfidenz`);
+    }
+
+    const longSteps = entries.filter(e => e.processingTimeMs > 5000);
+    if (longSteps.length > 0) {
+      potentialIssues.push(`${longSteps.length} Schritte benötigten mehr als 5 Sekunden`);
+    }
+
+    return {
+      analysisQuality,
+      keyInsights,
+      potentialIssues
+    };
+  }
+
+  getActionDisplayName(action: string): string {
+    return this.actionTranslations[action] || action;
+  }
+
+  formatDuration(ms: number): string {
+    if (ms < 1000) return '< 1s';
+    if (ms < 60000) return `${Math.ceil(ms / 1000)}s`;
+    const minutes = Math.floor(ms / 60000);
+    const seconds = Math.ceil((ms % 60000) / 1000);
+    return seconds > 0 ? `${minutes}m ${seconds}s` : `${minutes}m`;
+  }
+
+  getConfidenceColor(confidence: number): string {
+    if (confidence >= 80) return 'var(--color-accent)';
+    if (confidence >= 60) return 'var(--color-warning)';
+    return 'var(--color-error)';
+  }
+
+  isEnabled(): boolean {
+    return this.config.enabled;
+  }
+
+  getConfig(): AuditConfig {
+    return { ...this.config };
+  }
+}
+
+export const auditService = new AuditService();
+export type { ProcessedAuditTrail, CompressedAuditEntry };
+
+export const debugAuditService = {
+  getDebugInfo() {
+    return auditService.getDebugInfo();
+  },
+  isEnabled() {
+    return auditService.isEnabled();
+  },
+  getConfig() {
+    return auditService.getConfig();
+  }
+};
\ No newline at end of file
diff --git a/src/utils/auth.ts b/src/utils/auth.ts
index e11ca24..d2464be 100644
--- a/src/utils/auth.ts
+++ b/src/utils/auth.ts
@@ -260,8 +260,6 @@ function getAuthRequirement(context: AuthContextType): boolean {
       return process.env.AUTHENTICATION_NECESSARY_CONTRIBUTIONS !== 'false';
     case 'ai':
       return process.env.AUTHENTICATION_NECESSARY_AI !== 'false';
-    case 'general':
-      return process.env.AUTHENTICATION_NECESSARY !== 'false';
     default:
       return true;
   }
diff --git a/src/utils/clientUtils.ts b/src/utils/clientUtils.ts
new file mode 100644
index 0000000..0d766e1
--- /dev/null
+++ b/src/utils/clientUtils.ts
@@ -0,0 +1,366 @@
+// src/utils/clientUtils.ts
+// Client-side utilities that mirror server-side toolHelpers.ts
+
+export function createToolSlug(toolName: string): string {
+  if (!toolName || typeof toolName !== 'string') {
+    console.warn('[toolHelpers] Invalid toolName provided to createToolSlug:', toolName);
+    return '';
+  }
+  
+  return toolName.toLowerCase()
+    .replace(/[^a-z0-9\s-]/g, '')     // Remove special characters
+    .replace(/\s+/g, '-')             // Replace spaces with hyphens
+    .replace(/-+/g, '-')              // Remove duplicate hyphens
+    .replace(/^-|-$/g, '');           // Remove leading/trailing hyphens
+}
+
+export function findToolByIdentifier(tools: any[], identifier: string): any | undefined {
+  if (!identifier || !Array.isArray(tools)) return undefined;
+  
+  return tools.find((tool: any) => 
+    tool.name === identifier || 
+    createToolSlug(tool.name) === identifier.toLowerCase()
+  );
+}
+
+export function isToolHosted(tool: any): boolean {
+  return tool.projectUrl !== undefined && 
+         tool.projectUrl !== null && 
+         tool.projectUrl !== "" && 
+         tool.projectUrl.trim() !== "";
+}
+
+// Consolidated Autocomplete Functionality
+interface AutocompleteOptions {
+  minLength?: number;
+  maxResults?: number;
+  placeholder?: string;
+  allowMultiple?: boolean;
+  separator?: string;
+  filterFunction?: (query: string) => any[];
+  renderFunction?: (item: any) => string;
+  hiddenInput?: HTMLInputElement;
+}
+
+export class AutocompleteManager {
+  public input: HTMLInputElement;
+  public dataSource: any[];
+  public options: AutocompleteOptions;
+  public isOpen: boolean = false;
+  public selectedIndex: number = -1;
+  public filteredData: any[] = [];
+  public selectedItems: Set<string> = new Set();
+  public dropdown!: HTMLElement;
+  public selectedContainer!: HTMLElement;
+
+  constructor(inputElement: HTMLInputElement, dataSource: any[], options: AutocompleteOptions = {}) {
+    this.input = inputElement;
+    this.dataSource = dataSource;
+    this.options = {
+      minLength: 1,
+      maxResults: 10,
+      placeholder: 'Type to search...',
+      allowMultiple: false,
+      separator: ', ',
+      filterFunction: this.defaultFilter.bind(this),
+      renderFunction: this.defaultRender.bind(this),
+      ...options
+    };
+    
+    this.init();
+  }
+  
+  init(): void {
+    this.createDropdown();
+    this.bindEvents();
+    
+    if (this.options.allowMultiple) {
+      this.initMultipleMode();
+    }
+  }
+  
+  createDropdown(): void {
+    this.dropdown = document.createElement('div');
+    this.dropdown.className = 'autocomplete-dropdown';
+    this.dropdown.style.cssText = `
+      position: absolute;
+      top: 100%;
+      left: 0;
+      right: 0;
+      background: var(--color-bg);
+      border: 1px solid var(--color-border);
+      border-radius: 0.375rem;
+      box-shadow: var(--shadow-lg);
+      max-height: 200px;
+      overflow-y: auto;
+      z-index: 1000;
+      display: none;
+    `;
+    
+    // Insert dropdown after input
+    const parentElement = this.input.parentNode as HTMLElement;
+    parentElement.style.position = 'relative';
+    parentElement.insertBefore(this.dropdown, this.input.nextSibling);
+  }
+  
+  bindEvents(): void {
+    this.input.addEventListener('input', (e) => {
+      this.handleInput((e.target as HTMLInputElement).value);
+    });
+    
+    this.input.addEventListener('keydown', (e) => {
+      this.handleKeydown(e);
+    });
+    
+    this.input.addEventListener('focus', () => {
+      if (this.input.value.length >= (this.options.minLength || 1)) {
+        this.showDropdown();
+      }
+    });
+    
+    this.input.addEventListener('blur', () => {
+      // Delay to allow click events on dropdown items
+      setTimeout(() => {
+        const activeElement = document.activeElement;
+        if (!activeElement || !this.dropdown.contains(activeElement)) {
+          this.hideDropdown();
+        }
+      }, 150);
+    });
+    
+    document.addEventListener('click', (e) => {
+      const target = e.target as Node;
+      if (!this.input.contains(target) && !this.dropdown.contains(target)) {
+        this.hideDropdown();
+      }
+    });
+  }
+  
+  initMultipleMode(): void {
+    this.selectedContainer = document.createElement('div');
+    this.selectedContainer.className = 'autocomplete-selected';
+    this.selectedContainer.style.cssText = `
+      display: flex;
+      flex-wrap: wrap;
+      gap: 0.5rem;
+      margin-bottom: 0.5rem;
+      min-height: 1.5rem;
+    `;
+    
+    const parentElement = this.input.parentNode as HTMLElement;
+    parentElement.insertBefore(this.selectedContainer, this.input);
+    this.updateSelectedDisplay();
+  }
+  
+  handleInput(value: string): void {
+    if (value.length >= (this.options.minLength || 1)) {
+      this.filteredData = this.options.filterFunction!(value);
+      this.selectedIndex = -1;
+      this.renderDropdown();
+      this.showDropdown();
+    } else {
+      this.hideDropdown();
+    }
+  }
+  
+  handleKeydown(e: KeyboardEvent): void {
+    if (!this.isOpen) return;
+    
+    switch (e.key) {
+      case 'ArrowDown':
+        e.preventDefault();
+        this.selectedIndex = Math.min(this.selectedIndex + 1, this.filteredData.length - 1);
+        this.updateHighlight();
+        break;
+        
+      case 'ArrowUp':
+        e.preventDefault();
+        this.selectedIndex = Math.max(this.selectedIndex - 1, -1);
+        this.updateHighlight();
+        break;
+        
+      case 'Enter':
+        e.preventDefault();
+        if (this.selectedIndex >= 0) {
+          this.selectItem(this.filteredData[this.selectedIndex]);
+        }
+        break;
+        
+      case 'Escape':
+        this.hideDropdown();
+        break;
+    }
+  }
+  
+  defaultFilter(query: string): any[] {
+    const searchTerm = query.toLowerCase();
+    return this.dataSource
+      .filter(item => {
+        const text = typeof item === 'string' ? item : item.name || item.label || item.toString();
+        return text.toLowerCase().includes(searchTerm) && 
+               (!this.options.allowMultiple || !this.selectedItems.has(text));
+      })
+      .slice(0, this.options.maxResults || 10);
+  }
+  
+  defaultRender(item: any): string {
+    const text = typeof item === 'string' ? item : item.name || item.label || item.toString();
+    return `<div class="autocomplete-item">${this.escapeHtml(text)}</div>`;
+  }
+  
+  renderDropdown(): void {
+    if (this.filteredData.length === 0) {
+      this.dropdown.innerHTML = '<div class="autocomplete-no-results">No results found</div>';
+      return;
+    }
+    
+    this.dropdown.innerHTML = this.filteredData
+      .map((item, index) => {
+        const content = this.options.renderFunction!(item);
+        return `<div class="autocomplete-option" data-index="${index}" style="
+          padding: 0.5rem;
+          cursor: pointer;
+          border-bottom: 1px solid var(--color-border-light);
+          transition: background-color 0.15s ease;
+        ">${content}</div>`;
+      })
+      .join('');
+    
+    // Bind click events
+    this.dropdown.querySelectorAll('.autocomplete-option').forEach((option, index) => {
+      option.addEventListener('click', () => {
+        this.selectItem(this.filteredData[index]);
+      });
+      
+      option.addEventListener('mouseenter', () => {
+        this.selectedIndex = index;
+        this.updateHighlight();
+      });
+    });
+  }
+  
+  updateHighlight(): void {
+    this.dropdown.querySelectorAll('.autocomplete-option').forEach((option, index) => {
+      (option as HTMLElement).style.backgroundColor = index === this.selectedIndex 
+        ? 'var(--color-bg-secondary)' 
+        : 'transparent';
+    });
+  }
+  
+  selectItem(item: any): void {
+    const text = typeof item === 'string' ? item : item.name || item.label || item.toString();
+    
+    if (this.options.allowMultiple) {
+      this.selectedItems.add(text);
+      this.updateSelectedDisplay();
+      this.updateInputValue();
+      this.input.value = '';
+    } else {
+      this.input.value = text;
+      this.hideDropdown();
+    }
+    
+    // Trigger change event
+    this.input.dispatchEvent(new CustomEvent('autocomplete:select', {
+      detail: { item, text, selectedItems: Array.from(this.selectedItems) }
+    }));
+  }
+  
+  removeItem(text: string): void {
+    if (this.options.allowMultiple) {
+      this.selectedItems.delete(text);
+      this.updateSelectedDisplay();
+      this.updateInputValue();
+    }
+  }
+  
+  updateSelectedDisplay(): void {
+    if (!this.options.allowMultiple || !this.selectedContainer) return;
+    
+    this.selectedContainer.innerHTML = Array.from(this.selectedItems)
+      .map(item => `
+        <span class="autocomplete-tag" style="
+          background-color: var(--color-primary);
+          color: white;
+          padding: 0.25rem 0.5rem;
+          border-radius: 0.25rem;
+          font-size: 0.875rem;
+          display: flex;
+          align-items: center;
+          gap: 0.25rem;
+        ">
+          ${this.escapeHtml(item)}
+          <button type="button" class="autocomplete-remove" data-item="${this.escapeHtml(item)}" style="
+            background: none;
+            border: none;
+            color: white;
+            font-weight: bold;
+            cursor: pointer;
+            padding: 0;
+            width: 1rem;
+            height: 1rem;
+            display: flex;
+            align-items: center;
+            justify-content: center;
+          ">×</button>
+        </span>
+      `)
+      .join('');
+    
+    // Bind remove events
+    this.selectedContainer.querySelectorAll('.autocomplete-remove').forEach(btn => {
+      btn.addEventListener('click', (e) => {
+        e.preventDefault();
+        this.removeItem((btn as HTMLElement).getAttribute('data-item')!);
+      });
+    });
+  }
+  
+  updateInputValue(): void {
+    if (this.options.allowMultiple && this.options.hiddenInput) {
+      this.options.hiddenInput.value = Array.from(this.selectedItems).join(this.options.separator || ', ');
+    }
+  }
+  
+  showDropdown(): void {
+    this.dropdown.style.display = 'block';
+    this.isOpen = true;
+  }
+  
+  hideDropdown(): void {
+    this.dropdown.style.display = 'none';
+    this.isOpen = false;
+    this.selectedIndex = -1;
+  }
+  
+  escapeHtml(text: string): string {
+    const div = document.createElement('div');
+    div.textContent = text;
+    return div.innerHTML;
+  }
+  
+  setDataSource(newDataSource: any[]): void {
+    this.dataSource = newDataSource;
+  }
+  
+  getSelectedItems(): string[] {
+    return Array.from(this.selectedItems);
+  }
+  
+  setSelectedItems(items: string[]): void {
+    this.selectedItems = new Set(items);
+    if (this.options.allowMultiple) {
+      this.updateSelectedDisplay();
+      this.updateInputValue();
+    }
+  }
+  
+  destroy(): void {
+    if (this.dropdown && this.dropdown.parentNode) {
+      this.dropdown.parentNode.removeChild(this.dropdown);
+    }
+    if (this.selectedContainer && this.selectedContainer.parentNode) {
+      this.selectedContainer.parentNode.removeChild(this.selectedContainer);
+    }
+  }
+}
\ No newline at end of file
diff --git a/src/utils/dataService.ts b/src/utils/dataService.ts
index c0f8b1d..f80e541 100644
--- a/src/utils/dataService.ts
+++ b/src/utils/dataService.ts
@@ -128,7 +128,9 @@ async function loadRawData(): Promise<ToolsData> {
     const rawData = load(yamlContent);
     
     try {
+      console.log('Attempting to validate YAML structure...');
       cachedData = ToolsDataSchema.parse(rawData);
+      console.log('Validation successful!');
       
       if (!cachedData.skill_levels || Object.keys(cachedData.skill_levels).length === 0) {
         cachedData.skill_levels = {
@@ -144,8 +146,15 @@ async function loadRawData(): Promise<ToolsData> {
       console.log(`[DATA SERVICE] Loaded enhanced data version: ${dataVersion}`);
       
     } catch (error) {
-      console.error('YAML validation failed:', error);
-      throw new Error('Invalid tools.yaml structure');
+      if (error instanceof z.ZodError) {
+        console.error('ToolsDataSchema validation errors:');
+        error.errors.forEach((err, index) => {
+          console.error(`${index + 1}. Path: ${err.path.join('.')}, Error: ${err.message}`);
+        });
+      } else {
+        console.error('Non-Zod validation error:', error);
+      }
+      throw new Error(`Invalid tools.yaml structure: ${error}`);
     }
   }
   return cachedData;
diff --git a/src/utils/embeddings.ts b/src/utils/embeddings.ts
index 165fb90..c3745c6 100644
--- a/src/utils/embeddings.ts
+++ b/src/utils/embeddings.ts
@@ -2,6 +2,8 @@
 import { promises as fs } from 'fs';
 import path from 'path';
 import { getCompressedToolsDataForAI } from './dataService.js';
+import 'dotenv/config';
+import crypto from 'crypto';
 
 interface EmbeddingData {
   id: string;
@@ -35,12 +37,52 @@ class EmbeddingsService {
   private readonly embeddingsPath = path.join(process.cwd(), 'data', 'embeddings.json');
   private readonly batchSize: number;
   private readonly batchDelay: number;
-  private readonly enabled: boolean;
+  private enabled: boolean = false;
 
   constructor() {
-    this.enabled = process.env.AI_EMBEDDINGS_ENABLED === 'true';
     this.batchSize = parseInt(process.env.AI_EMBEDDINGS_BATCH_SIZE || '20', 10);
     this.batchDelay = parseInt(process.env.AI_EMBEDDINGS_BATCH_DELAY_MS || '1000', 10);
+    
+    this.enabled = true;
+  }
+
+  private async checkEnabledStatus(): Promise<void> {
+    try {
+      console.log('[EMBEDDINGS] Debug env check:', {
+        AI_EMBEDDINGS_ENABLED: process.env.AI_EMBEDDINGS_ENABLED,
+        envKeys: Object.keys(process.env).filter(k => k.includes('EMBEDDINGS')).length,
+        allEnvKeys: Object.keys(process.env).length
+      });
+      
+      const envEnabled = process.env.AI_EMBEDDINGS_ENABLED;
+      
+      if (envEnabled === 'true') {
+        const endpoint = process.env.AI_EMBEDDINGS_ENDPOINT;
+        const model = process.env.AI_EMBEDDINGS_MODEL;
+        
+        if (!endpoint || !model) {
+          console.warn('[EMBEDDINGS] Embeddings enabled but API configuration missing - disabling');
+          this.enabled = false;
+          return;
+        }
+        
+        console.log('[EMBEDDINGS] All requirements met - enabling embeddings');
+        this.enabled = true;
+        return;
+      }
+      
+      try {
+        await fs.stat(this.embeddingsPath);
+        console.log('[EMBEDDINGS] Existing embeddings file found - enabling');
+        this.enabled = true;
+      } catch {
+        console.log('[EMBEDDINGS] Embeddings not explicitly enabled - disabling');
+        this.enabled = false;
+      }
+    } catch (error) {
+      console.error('[EMBEDDINGS] Error checking enabled status:', error);
+      this.enabled = false;
+    }
   }
 
   async initialize(): Promise<void> {
@@ -57,60 +99,55 @@ class EmbeddingsService {
   }
 
   private async performInitialization(): Promise<void> {
+    await this.checkEnabledStatus();
     if (!this.enabled) {
       console.log('[EMBEDDINGS] Embeddings disabled, skipping initialization');
       return;
     }
 
+    const initStart = Date.now();
     try {
-      console.log('[EMBEDDINGS] Initializing embeddings system...');
-      
+      console.log('[EMBEDDINGS] Initializing embeddings system…');
+
       await fs.mkdir(path.dirname(this.embeddingsPath), { recursive: true });
-      
-      const toolsData = await getCompressedToolsDataForAI();
-      const currentDataHash = this.hashData(toolsData);
-      
-      const existingEmbeddings = await this.loadEmbeddings();
-      
-      if (existingEmbeddings && existingEmbeddings.version === currentDataHash) {
+
+      const toolsData        = await getCompressedToolsDataForAI();
+      const currentDataHash  = await this.hashToolsFile();
+
+      const existing         = await this.loadEmbeddings();
+      console.log('[EMBEDDINGS] Current hash:', currentDataHash);
+      console.log('[EMBEDDINGS] Existing file version:', existing?.version);
+      console.log('[EMBEDDINGS] Existing embeddings length:', existing?.embeddings?.length);
+
+      const cacheIsUsable =
+        existing &&
+        existing.version === currentDataHash &&
+        Array.isArray(existing.embeddings) &&
+        existing.embeddings.length > 0;
+
+      if (cacheIsUsable) {
         console.log('[EMBEDDINGS] Using cached embeddings');
-        this.embeddings = existingEmbeddings.embeddings;
+        this.embeddings    = existing.embeddings;
       } else {
-        console.log('[EMBEDDINGS] Generating new embeddings...');
+        console.log('[EMBEDDINGS] Generating new embeddings…');
         await this.generateEmbeddings(toolsData, currentDataHash);
       }
-      
+
       this.isInitialized = true;
-      console.log(`[EMBEDDINGS] Initialized with ${this.embeddings.length} embeddings`);
-      
-    } catch (error) {
-      console.error('[EMBEDDINGS] Failed to initialize:', error);
+      console.log(`[EMBEDDINGS] Initialized with ${this.embeddings.length} embeddings in ${Date.now() - initStart} ms`);
+    } catch (err) {
+      console.error('[EMBEDDINGS] Failed to initialize:', err);
       this.isInitialized = false;
-      throw error;
+      throw err;
     } finally {
       this.initializationPromise = null;
     }
   }
 
-  async waitForInitialization(): Promise<void> {
-    if (!this.enabled) {
-      return Promise.resolve();
-    }
-
-    if (this.isInitialized) {
-      return Promise.resolve();
-    }
-
-    if (this.initializationPromise) {
-      await this.initializationPromise;
-      return;
-    }
-
-    return this.initialize();
-  }
-
-  private hashData(data: any): string {
-    return Buffer.from(JSON.stringify(data)).toString('base64').slice(0, 32);
+  private async hashToolsFile(): Promise<string> {
+    const file = path.join(process.cwd(), 'src', 'data', 'tools.yaml');
+    const raw  = await fs.readFile(file, 'utf8');
+    return crypto.createHash('sha256').update(raw).digest('hex');
   }
 
   private async loadEmbeddings(): Promise<EmbeddingsDatabase | null> {
@@ -152,7 +189,10 @@ class EmbeddingsService {
     const model = process.env.AI_EMBEDDINGS_MODEL;
 
     if (!endpoint || !model) {
-      throw new Error('Missing embeddings API configuration');
+      const missing: string[] = [];
+      if (!endpoint) missing.push('AI_EMBEDDINGS_ENDPOINT');
+      if (!model) missing.push('AI_EMBEDDINGS_MODEL');
+      throw new Error(`Missing embeddings API configuration: ${missing.join(', ')}`);
     }
 
     const headers: Record<string, string> = {
@@ -240,10 +280,35 @@ class EmbeddingsService {
   }
 
   public async embedText(text: string): Promise<number[]> {
+    if (!this.enabled || !this.isInitialized) {
+      throw new Error('Embeddings service not available');
+    }
     const [embedding] = await this.generateEmbeddingsBatch([text.toLowerCase()]);
     return embedding;
   }
 
+  async waitForInitialization(): Promise<void> {
+    await this.checkEnabledStatus();
+    
+    if (!this.enabled || this.isInitialized) {
+      return Promise.resolve();
+    }
+
+    if (this.initializationPromise) {
+      await this.initializationPromise;
+      return;
+    }
+
+    return this.initialize();
+  }
+
+  async forceRecheckEnvironment(): Promise<void> {
+    this.enabled = false;
+    this.isInitialized = false;
+    await this.checkEnabledStatus();
+    console.log('[EMBEDDINGS] Environment status re-checked, enabled:', this.enabled);
+  }
+
   private cosineSimilarity(a: number[], b: number[]): number {
     let dotProduct = 0;
     let normA = 0;
@@ -259,54 +324,121 @@ class EmbeddingsService {
   }
 
   async findSimilar(query: string, maxResults: number = 30, threshold: number = 0.3): Promise<SimilarityResult[]> {
-    if (!this.enabled || !this.isInitialized || this.embeddings.length === 0) {
-      console.log('[EMBEDDINGS] Service not available for similarity search');
+    if (!this.enabled) {
+      console.log('[EMBEDDINGS] Service disabled for similarity search');
       return [];
     }
 
     try {
-      const queryEmbeddings = await this.generateEmbeddingsBatch([query.toLowerCase()]);
-      const queryEmbedding = queryEmbeddings[0];
-
-      console.log(`[EMBEDDINGS] Computing similarities for ${this.embeddings.length} items`);
-
-      const similarities: SimilarityResult[] = this.embeddings.map(item => ({
-        ...item,
-        similarity: this.cosineSimilarity(queryEmbedding, item.embedding)
-      }));
-
-      const results = similarities
-        .filter(item => item.similarity >= threshold)
-        .sort((a, b) => b.similarity - a.similarity) 
-        .slice(0, maxResults);
-
-      const orderingValid = results.every((item, index) => {
-        if (index === 0) return true;
-        return item.similarity <= results[index - 1].similarity;
-      });
-
-      if (!orderingValid) {
-        console.error('[EMBEDDINGS] CRITICAL: Similarity ordering is broken!');
-        results.forEach((item, idx) => {
-          console.error(`  ${idx}: ${item.name} = ${item.similarity.toFixed(4)}`);
-        });
-      }
-
-      console.log(`[EMBEDDINGS] Found ${results.length} similar items (threshold: ${threshold})`);
-      if (results.length > 0) {
-        console.log('[EMBEDDINGS] Top 10 similarity matches:');
-        results.slice(0, 10).forEach((item, idx) => {
-          console.log(`  ${idx + 1}. ${item.name} (${item.type}) = ${item.similarity.toFixed(4)}`);
-        });
+      if (this.isInitialized && this.embeddings.length > 0) {
+        console.log(`[EMBEDDINGS] Using embeddings data for similarity search: ${query}`);
         
-        const topSimilarity = results[0].similarity;
-        const hasHigherSimilarity = results.some(item => item.similarity > topSimilarity);
-        if (hasHigherSimilarity) {
-          console.error('[EMBEDDINGS] CRITICAL: Top result is not actually the highest similarity!');
-        }
-      }
+        const queryEmbeddings = await this.generateEmbeddingsBatch([query.toLowerCase()]);
+        const queryEmbedding = queryEmbeddings[0];
 
-      return results;
+        console.log(`[EMBEDDINGS] Computing similarities for ${this.embeddings.length} items`);
+
+        const similarities: SimilarityResult[] = this.embeddings.map(item => ({
+          ...item,
+          similarity: this.cosineSimilarity(queryEmbedding, item.embedding)
+        }));
+
+        const topScore      = Math.max(...similarities.map(s => s.similarity));
+        const dynamicCutOff = Math.max(threshold, topScore * 0.85);
+
+        const results = similarities
+          .filter(item => item.similarity >= dynamicCutOff)
+          .sort((a, b) => b.similarity - a.similarity)
+          .slice(0, maxResults);
+
+
+        const orderingValid = results.every((item, index) => {
+          if (index === 0) return true;
+          return item.similarity <= results[index - 1].similarity;
+        });
+
+        if (!orderingValid) {
+          console.error('[EMBEDDINGS] CRITICAL: Similarity ordering is broken!');
+          results.forEach((item, idx) => {
+            console.error(`  ${idx}: ${item.name} = ${item.similarity.toFixed(4)}`);
+          });
+        }
+
+        console.log(`[EMBEDDINGS] Found ${results.length} similar items (threshold: ${threshold})`);
+        if (results.length > 0) {
+          console.log('[EMBEDDINGS] Top 10 similarity matches:');
+          results.slice(0, 10).forEach((item, idx) => {
+            console.log(`  ${idx + 1}. ${item.name} (${item.type}) = ${item.similarity.toFixed(4)}`);
+          });
+          
+          const topSimilarity = results[0].similarity;
+          const hasHigherSimilarity = results.some(item => item.similarity > topSimilarity);
+          if (hasHigherSimilarity) {
+            console.error('[EMBEDDINGS] CRITICAL: Top result is not actually the highest similarity!');
+          }
+        }
+
+        return results;
+
+      } else {
+        console.log(`[EMBEDDINGS] No embeddings data, using fallback text matching: ${query}`);
+        
+        const { getToolsData } = await import('./dataService.js');
+        const toolsData = await getToolsData();
+        
+        const queryLower = query.toLowerCase();
+        const queryWords = queryLower.split(/\s+/).filter(w => w.length > 2);
+        
+        const similarities: SimilarityResult[] = toolsData.tools
+          .map((tool: any) => {
+            let similarity = 0;
+            
+            if (tool.name.toLowerCase().includes(queryLower)) {
+              similarity += 0.8;
+            }
+            
+            if (tool.description && tool.description.toLowerCase().includes(queryLower)) {
+              similarity += 0.6;
+            }
+            
+            if (tool.tags && Array.isArray(tool.tags)) {
+              const matchingTags = tool.tags.filter((tag: string) => 
+                tag.toLowerCase().includes(queryLower) || queryLower.includes(tag.toLowerCase())
+              );
+              if (tool.tags.length > 0) {
+                similarity += (matchingTags.length / tool.tags.length) * 0.4;
+              }
+            }
+            
+            const toolText = `${tool.name} ${tool.description || ''} ${(tool.tags || []).join(' ')}`.toLowerCase();
+            const matchingWords = queryWords.filter(word => toolText.includes(word));
+            if (queryWords.length > 0) {
+              similarity += (matchingWords.length / queryWords.length) * 0.3;
+            }
+            
+            return {
+              id: `tool_${tool.name.replace(/[^a-zA-Z0-9]/g, '_').toLowerCase()}`,
+              type: 'tool' as const,
+              name: tool.name,
+              content: toolText,
+              embedding: [],
+              metadata: {
+                domains: tool.domains || [],
+                phases: tool.phases || [],
+                tags: tool.tags || [],
+                skillLevel: tool.skillLevel,
+                type: tool.type
+              },
+              similarity: Math.min(similarity, 1.0)
+            };
+          })
+          .filter(item => item.similarity >= threshold)
+          .sort((a, b) => b.similarity - a.similarity)
+          .slice(0, maxResults);
+
+        console.log(`[EMBEDDINGS] Fallback found ${similarities.length} similar items`);
+        return similarities;
+      }
 
     } catch (error) {
       console.error('[EMBEDDINGS] Failed to find similar items:', error);
@@ -315,7 +447,11 @@ class EmbeddingsService {
   }
 
   isEnabled(): boolean {
-    return this.enabled && this.isInitialized;
+    if (!this.enabled && !this.isInitialized) {
+      this.checkEnabledStatus().catch(console.error);
+    }
+    
+    return this.enabled;
   }
 
   getStats(): { enabled: boolean; initialized: boolean; count: number } {
@@ -327,14 +463,15 @@ class EmbeddingsService {
   }
 }
 
-
-
 const embeddingsService = new EmbeddingsService();
 
 export { embeddingsService, type EmbeddingData, type SimilarityResult };
 
-if (typeof window === 'undefined' && process.env.NODE_ENV !== 'test') {
-  embeddingsService.initialize().catch(error => {
-    console.error('[EMBEDDINGS] Auto-initialization failed:', error);
-  });
-}
\ No newline at end of file
+export const debugEmbeddings = {
+  async recheckEnvironment() {
+    return embeddingsService.forceRecheckEnvironment();
+  },
+  getStatus() {
+    return embeddingsService.getStats();
+  }
+};
\ No newline at end of file
diff --git a/src/utils/gitContributions.ts b/src/utils/gitContributions.ts
index 426f884..66382c3 100644
--- a/src/utils/gitContributions.ts
+++ b/src/utils/gitContributions.ts
@@ -5,22 +5,23 @@ export interface ContributionData {
   type: 'add' | 'edit';
   tool: {
     name: string;
-    icon?: string;
+    icon?: string | null;
     type: 'software' | 'method' | 'concept';
     description: string;
     domains: string[];
     phases: string[];
     platforms: string[];
     skillLevel: string;
-    accessType?: string;
+    accessType?: string | null;
     url: string;
-    projectUrl?: string;
-    license?: string;
-    knowledgebase?: boolean;
-    'domain-agnostic-software'?: string[];
-    related_concepts?: string[];
+    projectUrl?: string | null;
+    license?: string | null;
+    knowledgebase?: boolean | null;
+    'domain-agnostic-software'?: string[] | null;
+    related_concepts?: string[] | null;
+    related_software?: string[] | null;
     tags: string[];
-    statusUrl?: string;
+    statusUrl?: string | null;
   };
   metadata: {
     submitter: string;
@@ -134,6 +135,7 @@ export class GitContributionManager {
     if (tool.projectUrl) cleanTool.projectUrl = tool.projectUrl;
     if (tool.knowledgebase) cleanTool.knowledgebase = tool.knowledgebase;
     if (tool.related_concepts?.length) cleanTool.related_concepts = tool.related_concepts;
+    if (tool.related_software?.length) cleanTool.related_software = tool.related_software;
     if (tool.tags?.length) cleanTool.tags = tool.tags;
     if (tool['domain-agnostic-software']?.length) {
       cleanTool['domain-agnostic-software'] = tool['domain-agnostic-software'];
@@ -272,6 +274,8 @@ ${data.tool.platforms?.length ? `- **Platforms:** ${data.tool.platforms.join(',
 ${data.tool.license ? `- **License:** ${data.tool.license}` : ''}
 ${data.tool.domains?.length ? `- **Domains:** ${data.tool.domains.join(', ')}` : ''}
 ${data.tool.phases?.length ? `- **Phases:** ${data.tool.phases.join(', ')}` : ''}
+${data.tool.related_concepts?.length ? `- **Related Concepts:** ${data.tool.related_concepts.join(', ')}` : ''}
+${data.tool.related_software?.length ? `- **Related Software:** ${data.tool.related_software.join(', ')}` : ''}
 
 ${data.metadata.reason ? `### Reason
 ${data.metadata.reason}
@@ -299,9 +303,6 @@ ${data.metadata.contact}
   private generateKnowledgebaseIssueBody(data: KnowledgebaseContribution): string {
     const sections: string[] = [];
 
-    /* ------------------------------------------------------------------ */
-    /* Header                                                             */
-    /* ------------------------------------------------------------------ */
     sections.push(`## Knowledge Base Article: ${data.title ?? 'Untitled'}`);
     sections.push('');
     sections.push(`**Submitted by:** ${data.submitter}`);
@@ -310,18 +311,12 @@ ${data.metadata.contact}
     if (data.difficulty) sections.push(`**Difficulty:** ${data.difficulty}`);
     sections.push('');
 
-    /* ------------------------------------------------------------------ */
-    /* Description                                                        */
-    /* ------------------------------------------------------------------ */
     if (data.description) {
       sections.push('### Description');
       sections.push(data.description);
       sections.push('');
     }
 
-    /* ------------------------------------------------------------------ */
-    /* Content                                                            */
-    /* ------------------------------------------------------------------ */
     if (data.content) {
       sections.push('### Article Content');
       sections.push('```markdown');
@@ -330,18 +325,12 @@ ${data.metadata.contact}
       sections.push('');
     }
 
-    /* ------------------------------------------------------------------ */
-    /* External resources                                                 */
-    /* ------------------------------------------------------------------ */
     if (data.externalLink) {
       sections.push('### External Resource');
       sections.push(`- [External Documentation](${data.externalLink})`);
       sections.push('');
     }
 
-    /* ------------------------------------------------------------------ */
-    /* Uploaded files                                                     */
-    /* ------------------------------------------------------------------ */
     if (Array.isArray(data.uploadedFiles) && data.uploadedFiles.length) {
       sections.push('### Uploaded Files');
       data.uploadedFiles.forEach((file) => {
@@ -359,9 +348,6 @@ ${data.metadata.contact}
       sections.push('');
     }
 
-    /* ------------------------------------------------------------------ */
-    /* Categories & Tags                                                  */
-    /* ------------------------------------------------------------------ */
     const hasCategories = Array.isArray(data.categories) && data.categories.length > 0;
     const hasTags       = Array.isArray(data.tags)       && data.tags.length > 0;
 
@@ -372,18 +358,12 @@ ${data.metadata.contact}
       sections.push('');
     }
 
-    /* ------------------------------------------------------------------ */
-    /* Reason                                                             */
-    /* ------------------------------------------------------------------ */
     if (data.reason) {
       sections.push('### Reason for Contribution');
       sections.push(data.reason);
       sections.push('');
     }
 
-    /* ------------------------------------------------------------------ */
-    /* Footer                                                             */
-    /* ------------------------------------------------------------------ */
     sections.push('### For Maintainers');
     sections.push('1. Review the content for quality and accuracy');
     sections.push('2. Create the appropriate markdown file in `src/content/knowledgebase/`');
@@ -395,4 +375,4 @@ ${data.metadata.contact}
 
     return sections.join('\n');
   }
-}
+}
\ No newline at end of file
diff --git a/src/utils/nextcloud.ts b/src/utils/nextcloud.ts
index 5d2ae38..4bcb819 100644
--- a/src/utils/nextcloud.ts
+++ b/src/utils/nextcloud.ts
@@ -338,7 +338,7 @@ export class NextcloudUploader {
           info: {
             path: remotePath,
             exists: true,
-            response: text.substring(0, 200) + '...' // Truncated for safety
+            response: text.substring(0, 200) + '...'
           }
         };
       }
diff --git a/tools-yaml-editor.html b/tools-yaml-editor.html
new file mode 100644
index 0000000..60dc576
--- /dev/null
+++ b/tools-yaml-editor.html
@@ -0,0 +1,2082 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Tools.yaml Editor</title>
+    <script src="https://cdnjs.cloudflare.com/ajax/libs/js-yaml/4.1.0/js-yaml.min.js"></script>
+    <style>
+        :root {
+            --bg-primary: #ffffff;
+            --bg-secondary: #f8f9fa;
+            --bg-tertiary: #e9ecef;
+            --text-primary: #212529;
+            --text-secondary: #6c757d;
+            --border-color: #dee2e6;
+            --accent-color: #0d6efd;
+            --success-color: #198754;
+            --warning-color: #fd7e14;
+            --danger-color: #dc3545;
+            --shadow: 0 2px 4px rgba(0,0,0,0.1);
+        }
+
+        [data-theme="dark"] {
+            --bg-primary: #1a1a1a;
+            --bg-secondary: #2d2d2d;
+            --bg-tertiary: #404040;
+            --text-primary: #ffffff;
+            --text-secondary: #b0b0b0;
+            --border-color: #404040;
+            --accent-color: #4a9eff;
+            --success-color: #28a745;
+            --warning-color: #ffc107;
+            --danger-color: #dc3545;
+            --shadow: 0 2px 4px rgba(0,0,0,0.3);
+        }
+
+        * {
+            margin: 0;
+            padding: 0;
+            box-sizing: border-box;
+        }
+
+        body {
+            font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif;
+            background-color: var(--bg-primary);
+            color: var(--text-primary);
+            line-height: 1.5;
+            min-width: 1200px;
+        }
+
+        .container {
+            max-width: 1600px;
+            margin: 0 auto;
+            padding: 20px;
+        }
+
+        .header {
+            display: flex;
+            justify-content: space-between;
+            align-items: center;
+            margin-bottom: 20px;
+            padding: 20px;
+            background: var(--bg-secondary);
+            border-radius: 8px;
+            box-shadow: var(--shadow);
+        }
+
+        .controls {
+            display: flex;
+            gap: 15px;
+            align-items: center;
+        }
+
+        .search-section {
+            display: grid;
+            grid-template-columns: 1fr auto;
+            gap: 20px;
+            margin-bottom: 20px;
+            padding: 20px;
+            background: var(--bg-secondary);
+            border-radius: 8px;
+            box-shadow: var(--shadow);
+        }
+
+        .search-filters {
+            display: grid;
+            grid-template-columns: 2fr 1fr 1fr 1fr 1fr 1fr 1fr;
+            gap: 15px;
+            align-items: end;
+        }
+
+        .bulk-actions {
+            display: flex;
+            gap: 10px;
+        }
+
+        .main-content {
+            display: grid;
+            grid-template-columns: 1fr 500px;
+            gap: 20px;
+        }
+
+        .tools-section {
+            background: var(--bg-secondary);
+            border-radius: 8px;
+            padding: 20px;
+            box-shadow: var(--shadow);
+        }
+
+        .edit-section {
+            background: var(--bg-secondary);
+            border-radius: 8px;
+            padding: 20px;
+            box-shadow: var(--shadow);
+            position: sticky;
+            top: 20px;
+            max-height: calc(100vh - 40px);
+            overflow-y: auto;
+        }
+
+        .tools-header {
+            display: flex;
+            justify-content: space-between;
+            align-items: center;
+            margin-bottom: 15px;
+        }
+
+        .tools-list {
+            max-height: 70vh;
+            overflow-y: auto;
+        }
+
+        .tool-item {
+            padding: 15px;
+            border: 1px solid var(--border-color);
+            border-radius: 6px;
+            margin-bottom: 10px;
+            cursor: pointer;
+            transition: all 0.2s;
+            background: var(--bg-primary);
+        }
+
+        .tool-item:hover {
+            background: var(--bg-tertiary);
+        }
+
+        .tool-item.selected {
+            border-color: var(--accent-color);
+            background: var(--bg-tertiary);
+        }
+
+        .tool-header {
+            display: flex;
+            justify-content: space-between;
+            align-items: flex-start;
+            margin-bottom: 8px;
+        }
+
+        .tool-name {
+            font-weight: 600;
+            color: var(--accent-color);
+            margin-bottom: 4px;
+        }
+
+        .tool-meta {
+            display: flex;
+            gap: 10px;
+            font-size: 0.85em;
+            color: var(--text-secondary);
+        }
+
+        .tool-description {
+            font-size: 0.9em;
+            margin-top: 8px;
+            display: -webkit-box;
+            -webkit-box-orient: vertical;
+            overflow: hidden;
+        }
+
+        .tags {
+            display: flex;
+            flex-wrap: wrap;
+            gap: 4px;
+            margin-top: 8px;
+        }
+
+        .tag {
+            background: var(--accent-color);
+            color: white;
+            padding: 2px 8px;
+            border-radius: 12px;
+            font-size: 0.75em;
+        }
+
+        .form-group {
+            margin-bottom: 15px;
+        }
+
+        .form-label {
+            display: block;
+            margin-bottom: 5px;
+            font-weight: 500;
+        }
+
+        .form-input, .form-select, .form-textarea {
+            width: 100%;
+            padding: 8px 12px;
+            border: 1px solid var(--border-color);
+            border-radius: 4px;
+            background: var(--bg-primary);
+            color: var(--text-primary);
+            font-size: 14px;
+        }
+
+        .form-textarea {
+            resize: vertical;
+            min-height: 80px;
+        }
+
+        .form-checkbox {
+            margin-right: 8px;
+        }
+
+        .btn {
+            padding: 8px 16px;
+            border: none;
+            border-radius: 4px;
+            cursor: pointer;
+            font-size: 14px;
+            transition: all 0.2s;
+            text-decoration: none;
+            display: inline-block;
+            text-align: center;
+        }
+
+        .btn-primary {
+            background: var(--accent-color);
+            color: white;
+        }
+
+        .btn-secondary {
+            background: var(--bg-tertiary);
+            color: var(--text-primary);
+            border: 1px solid var(--border-color);
+        }
+
+        .btn-success {
+            background: var(--success-color);
+            color: white;
+        }
+
+        .btn-warning {
+            background: var(--warning-color);
+            color: white;
+        }
+
+        .btn-danger {
+            background: var(--danger-color);
+            color: white;
+        }
+
+        .btn-sm {
+            padding: 4px 8px;
+            font-size: 12px;
+        }
+
+        .btn:hover {
+            opacity: 0.9;
+            transform: translateY(-1px);
+        }
+
+        .autocomplete-container {
+            position: relative;
+        }
+
+        .autocomplete-dropdown {
+            position: absolute;
+            top: 100%;
+            left: 0;
+            right: 0;
+            background: var(--bg-primary);
+            border: 1px solid var(--border-color);
+            border-top: none;
+            border-radius: 0 0 4px 4px;
+            max-height: 200px;
+            overflow-y: auto;
+            z-index: 1000;
+            display: none;
+        }
+
+        .autocomplete-item {
+            padding: 8px 12px;
+            cursor: pointer;
+            border-bottom: 1px solid var(--border-color);
+        }
+
+        .autocomplete-item:hover,
+        .autocomplete-item.selected {
+            background: var(--bg-tertiary);
+        }
+
+        .autocomplete-item:last-child {
+            border-bottom: none;
+        }
+
+        .status-bar {
+            position: fixed;
+            bottom: 0;
+            left: 0;
+            right: 0;
+            background: var(--bg-secondary);
+            border-top: 1px solid var(--border-color);
+            padding: 10px 20px;
+            display: flex;
+            justify-content: space-between;
+            align-items: center;
+            font-size: 0.9em;
+        }
+
+        .theme-toggle {
+            background: none;
+            border: 1px solid var(--border-color);
+            color: var(--text-primary);
+            border-radius: 4px;
+            cursor: pointer;
+            padding: 6px 10px;
+        }
+
+        .validation-errors {
+            background: var(--danger-color);
+            color: white;
+            padding: 10px;
+            border-radius: 4px;
+            margin-bottom: 15px;
+            display: none;
+        }
+
+        .stats {
+            display: grid;
+            grid-template-columns: repeat(auto-fit, minmax(120px, 1fr));
+            gap: 10px;
+            margin-bottom: 20px;
+        }
+
+        .stat-item {
+            text-align: center;
+            padding: 10px;
+            background: var(--bg-tertiary);
+            border-radius: 4px;
+        }
+
+        .stat-number {
+            font-size: 1.5em;
+            font-weight: bold;
+            color: var(--accent-color);
+        }
+
+        .bulk-panel {
+            display: none;
+            background: var(--bg-tertiary);
+            padding: 15px;
+            border-radius: 6px;
+            margin-top: 15px;
+        }
+
+        .bulk-panel.active {
+            display: block;
+        }
+
+        .tag-input-container {
+            position: relative;
+        }
+
+        .tag-input {
+            display: flex;
+            flex-wrap: wrap;
+            gap: 4px;
+            min-height: 36px;
+            padding: 4px 8px;
+            border: 1px solid var(--border-color);
+            border-radius: 4px;
+            background: var(--bg-primary);
+            cursor: text;
+        }
+
+        .tag-input-item {
+            background: var(--accent-color);
+            color: white;
+            padding: 2px 8px;
+            border-radius: 12px;
+            font-size: 0.8em;
+            display: flex;
+            align-items: center;
+            gap: 4px;
+        }
+
+        .tag-input-item .remove {
+            cursor: pointer;
+            font-weight: bold;
+        }
+
+        .tag-input input {
+            border: none;
+            background: none;
+            outline: none;
+            color: var(--text-primary);
+            min-width: 100px;
+            flex: 1;
+        }
+
+        .section-tabs {
+            display: flex;
+            margin-bottom: 20px;
+            border-bottom: 1px solid var(--border-color);
+        }
+
+        .section-tab {
+            padding: 10px 20px;
+            background: none;
+            border: none;
+            color: var(--text-secondary);
+            cursor: pointer;
+            border-bottom: 2px solid transparent;
+            transition: all 0.2s;
+        }
+
+        .section-tab.active {
+            color: var(--accent-color);
+            border-bottom-color: var(--accent-color);
+        }
+
+        .section-content {
+            display: none;
+        }
+
+        .section-content.active {
+            display: block;
+        }
+
+        .categories-list {
+            max-height: 300px;
+            overflow-y: auto;
+            border: 1px solid var(--border-color);
+            border-radius: 4px;
+            margin-bottom: 15px;
+        }
+
+        .category-item {
+            padding: 10px;
+            border-bottom: 1px solid var(--border-color);
+            cursor: pointer;
+            transition: background 0.2s;
+        }
+
+        .category-item:hover {
+            background: var(--bg-tertiary);
+        }
+
+        .category-item:last-child {
+            border-bottom: none;
+        }
+
+        .category-item.selected {
+            background: var(--bg-tertiary);
+            border-left: 3px solid var(--accent-color);
+        }
+
+        .category-name {
+            font-weight: 600;
+            margin-bottom: 4px;
+        }
+
+        .category-description {
+            font-size: 0.9em;
+            color: var(--text-secondary);
+        }
+    </style>
+</head>
+<body data-theme="dark">
+    <div class="container">
+        <header class="header">
+            <h1>🔧 Tools.yaml Editor</h1>
+            <div class="controls">
+                <button class="theme-toggle" onclick="toggleTheme()">☀️</button>
+                <input type="file" id="importFile" accept=".yaml,.yml" style="display: none;" onchange="importYAML(event)">
+                <button class="btn btn-secondary" onclick="document.getElementById('importFile').click()">Import YAML</button>
+                <button class="btn btn-primary" onclick="exportYAML()">Export YAML</button>
+                <button class="btn btn-success" onclick="addNewTool()">Add Tool</button>
+            </div>
+        </header>
+
+        <section class="search-section">
+            <div class="search-filters">
+                <div class="form-group">
+                    <label class="form-label">Search Tools</label>
+                    <div class="autocomplete-container">
+                        <input type="text" id="searchInput" class="form-input" placeholder="Search by name, description, tags..." oninput="filterTools()">
+                        <div class="autocomplete-dropdown" id="searchAutocomplete"></div>
+                    </div>
+                </div>
+                <div class="form-group">
+                    <label class="form-label">Type</label>
+                    <select id="typeFilter" class="form-select" onchange="filterTools()">
+                        <option value="">All Types</option>
+                        <option value="software">Software</option>
+                        <option value="method">Method</option>
+                        <option value="concept">Concept</option>
+                    </select>
+                </div>
+                <div class="form-group">
+                    <label class="form-label">Skill Level</label>
+                    <select id="skillFilter" class="form-select" onchange="filterTools()">
+                        <option value="">All Levels</option>
+                        <option value="novice">Novice</option>
+                        <option value="beginner">Beginner</option>
+                        <option value="intermediate">Intermediate</option>
+                        <option value="advanced">Advanced</option>
+                        <option value="expert">Expert</option>
+                    </select>
+                </div>
+                <div class="form-group">
+                    <label class="form-label">Domain</label>
+                    <select id="domainFilter" class="form-select" onchange="filterTools()">
+                        <option value="">All Domains</option>
+                    </select>
+                </div>
+                <div class="form-group">
+                    <label class="form-label">Knowledgebase</label>
+                    <select id="knowledgebaseFilter" class="form-select" onchange="filterTools()">
+                        <option value="">All</option>
+                        <option value="true">Has KB</option>
+                        <option value="false">No KB</option>
+                    </select>
+                </div>
+                <div class="form-group">
+                    <label class="form-label">Validation</label>
+                    <select id="validationFilter" class="form-select" onchange="filterTools()">
+                        <option value="">All</option>
+                        <option value="valid">Valid Only</option>
+                        <option value="invalid">Has Issues</option>
+                    </select>
+                </div>
+                <div class="form-group">
+                    <label class="form-label">Sort by</label>
+                    <select id="sortBy" class="form-select" onchange="sortTools()">
+                        <option value="name">Name</option>
+                        <option value="type">Type</option>
+                        <option value="skillLevel">Skill Level</option>
+                        <option value="domains">Domain</option>
+                    </select>
+                </div>
+            </div>
+            <div class="bulk-actions">
+                <button class="btn btn-warning btn-sm" onclick="validateAllTools()">Validate All</button>
+                <button class="btn btn-secondary btn-sm" onclick="clearAllFilters()">Clear Filters</button>
+                <button class="btn btn-secondary btn-sm" onclick="toggleBulkPanel()">Bulk Actions</button>
+                <button class="btn btn-secondary btn-sm" onclick="showStats()">Statistics</button>
+            </div>
+        </section>
+
+        <div class="bulk-panel" id="bulkPanel">
+            <h3>Bulk Operations</h3>
+            <div style="display: grid; grid-template-columns: repeat(auto-fit, minmax(200px, 1fr)); gap: 15px; margin-top: 10px;">
+                <div>
+                    <button class="btn btn-warning btn-sm" onclick="bulkRemoveTags()">Remove Tags</button>
+                    <input type="text" id="bulkRemoveTagsInput" placeholder="Tags to remove (comma-separated)" class="form-input" style="margin-top: 5px; font-size: 12px;">
+                </div>
+                <div>
+                    <button class="btn btn-warning btn-sm" onclick="bulkRenameTags()">Rename Tags</button>
+                    <input type="text" id="bulkRenameFrom" placeholder="From tag" class="form-input" style="margin-top: 5px; font-size: 12px;">
+                    <input type="text" id="bulkRenameTo" placeholder="To tag" class="form-input" style="margin-top: 5px; font-size: 12px;">
+                </div>
+                <div>
+                    <button class="btn btn-success btn-sm" onclick="bulkAssignRelatedConcepts()">Assign Related Concepts</button>
+                    <input type="text" id="bulkConceptsInput" placeholder="Concepts (comma-separated)" class="form-input" style="margin-top: 5px; font-size: 12px;">
+                </div>
+                <div>
+                    <button class="btn btn-success btn-sm" onclick="bulkAssignRelatedSoftware()">Assign Related Software</button>
+                    <input type="text" id="bulkSoftwareInput" placeholder="Software (comma-separated)" class="form-input" style="margin-top: 5px; font-size: 12px;">
+                </div>
+            </div>
+        </div>
+
+        <div class="main-content">
+            <section class="tools-section">
+                <div class="tools-header">
+                    <h2>Tools (<span id="toolCount">0</span>)</h2>
+                    <div style="display: flex; gap: 10px; align-items: center;">
+                        <div id="validationSummary" style="display: none; padding: 5px 10px; background: var(--warning-color); color: white; border-radius: 4px; font-size: 0.9em;">
+                            <span id="validationCount">0</span> tools need fixing
+                        </div>
+                        <button class="btn btn-danger btn-sm" onclick="clearSelection()">Clear Selection</button>
+                    </div>
+                </div>
+                <div class="stats" id="statsContainer" style="display: none;"></div>
+                <div class="validation-errors" id="validationErrors"></div>
+                <div class="tools-list" id="toolsList"></div>
+            </section>
+
+            <section class="edit-section">
+                <div class="section-tabs">
+                    <button class="section-tab active" onclick="switchSection('tools')">Tools</button>
+                    <button class="section-tab" onclick="switchSection('domains')">Domains</button>
+                    <button class="section-tab" onclick="switchSection('phases')">Phases</button>
+                    <button class="section-tab" onclick="switchSection('categories')">Domain-Agnostic</button>
+                    <button class="section-tab" onclick="switchSection('scenarios')">Scenarios</button>
+                </div>
+
+                <!-- Tools Section -->
+                <div id="tools-section" class="section-content active">
+                    <h2 id="editTitle">Tool Editor</h2>
+                    <form id="toolForm">
+                        <div class="form-group">
+                            <label class="form-label">Name *</label>
+                            <input type="text" id="name" class="form-input" required>
+                        </div>
+
+                        <div class="form-group">
+                            <label class="form-label">Type *</label>
+                            <select id="type" class="form-select" required>
+                                <option value="">Select type...</option>
+                                <option value="software">Software</option>
+                                <option value="method">Method</option>
+                                <option value="concept">Concept</option>
+                            </select>
+                        </div>
+
+                        <div class="form-group">
+                            <label class="form-label">Icon</label>
+                            <input type="text" id="icon" class="form-input" placeholder="🔧">
+                        </div>
+
+                        <div class="form-group">
+                            <label class="form-label">Description *</label>
+                            <textarea id="description" class="form-textarea" required placeholder="Detailed description of the tool..."></textarea>
+                        </div>
+
+                        <div class="form-group">
+                            <label class="form-label">URL *</label>
+                            <input type="url" id="url" class="form-input" required placeholder="https://example.com">
+                        </div>
+
+                        <div class="form-group">
+                            <label class="form-label">Project URL</label>
+                            <input type="url" id="projectUrl" class="form-input" placeholder="https://project.example.com">
+                        </div>
+
+                        <div class="form-group">
+                            <label class="form-label">Status URL</label>
+                            <input type="url" id="statusUrl" class="form-input" placeholder="https://status.example.com">
+                        </div>
+
+                        <div class="form-group">
+                            <label class="form-label">Skill Level *</label>
+                            <select id="skillLevel" class="form-select" required>
+                                <option value="">Select level...</option>
+                                <option value="novice">Novice</option>
+                                <option value="beginner">Beginner</option>
+                                <option value="intermediate">Intermediate</option>
+                                <option value="advanced">Advanced</option>
+                                <option value="expert">Expert</option>
+                            </select>
+                        </div>
+
+                        <div class="form-group">
+                            <label class="form-label">License</label>
+                            <input type="text" id="license" class="form-input" placeholder="MIT, GPL-3.0, Proprietary...">
+                        </div>
+
+                        <div class="form-group">
+                            <label class="form-label">Access Type</label>
+                            <select id="accessType" class="form-select">
+                                <option value="">Select access type...</option>
+                                <option value="download">Download</option>
+                                <option value="web">Web</option>
+                                <option value="api">API</option>
+                                <option value="cli">CLI</option>
+                                <option value="commercial">Commercial</option>
+                                <option value="server-based">Server-based</option>
+                            </select>
+                        </div>
+
+                        <div class="form-group">
+                            <label class="form-label">Domains</label>
+                            <div class="tag-input-container">
+                                <div class="tag-input" id="domainsInput" onclick="focusTagInput('domainsInput')">
+                                    <input type="text" placeholder="Type domain..." onkeydown="handleTagInput(event, 'domains')" oninput="showAutocomplete(event, 'domains')">
+                                </div>
+                                <div class="autocomplete-dropdown" id="domainsAutocomplete"></div>
+                            </div>
+                        </div>
+
+                        <div class="form-group">
+                            <label class="form-label">Phases</label>
+                            <div class="tag-input-container">
+                                <div class="tag-input" id="phasesInput" onclick="focusTagInput('phasesInput')">
+                                    <input type="text" placeholder="Type phase..." onkeydown="handleTagInput(event, 'phases')" oninput="showAutocomplete(event, 'phases')">
+                                </div>
+                                <div class="autocomplete-dropdown" id="phasesAutocomplete"></div>
+                            </div>
+                        </div>
+
+                        <div class="form-group">
+                            <label class="form-label">Platforms</label>
+                            <div class="tag-input-container">
+                                <div class="tag-input" id="platformsInput" onclick="focusTagInput('platformsInput')">
+                                    <input type="text" placeholder="Type platform..." onkeydown="handleTagInput(event, 'platforms')" oninput="showAutocomplete(event, 'platforms')">
+                                </div>
+                                <div class="autocomplete-dropdown" id="platformsAutocomplete"></div>
+                            </div>
+                        </div>
+
+                        <div class="form-group">
+                            <label class="form-label">Domain-Agnostic Categories</label>
+                            <div class="tag-input-container">
+                                <div class="tag-input" id="domain-agnostic-softwareInput" onclick="focusTagInput('domain-agnostic-softwareInput')">
+                                    <input type="text" placeholder="Type category..." onkeydown="handleTagInput(event, 'domain-agnostic-software')" oninput="showAutocomplete(event, 'domain-agnostic-software')">
+                                </div>
+                                <div class="autocomplete-dropdown" id="domain-agnostic-softwareAutocomplete"></div>
+                            </div>
+                        </div>
+
+                        <div class="form-group">
+                            <label class="form-label">Tags</label>
+                            <div class="tag-input-container">
+                                <div class="tag-input" id="tagsInput" onclick="focusTagInput('tagsInput')">
+                                    <input type="text" placeholder="Type tag..." onkeydown="handleTagInput(event, 'tags')" oninput="showAutocomplete(event, 'tags')">
+                                </div>
+                                <div class="autocomplete-dropdown" id="tagsAutocomplete"></div>
+                            </div>
+                        </div>
+
+                        <div class="form-group">
+                            <label class="form-label">Related Concepts</label>
+                            <div class="tag-input-container">
+                                <div class="tag-input" id="related_conceptsInput" onclick="focusTagInput('related_conceptsInput')">
+                                    <input type="text" placeholder="Type concept..." onkeydown="handleTagInput(event, 'related_concepts')" oninput="showAutocomplete(event, 'related_concepts')">
+                                </div>
+                                <div class="autocomplete-dropdown" id="related_conceptsAutocomplete"></div>
+                            </div>
+                        </div>
+
+                        <div class="form-group">
+                            <label class="form-label">Related Software</label>
+                            <div class="tag-input-container">
+                                <div class="tag-input" id="related_softwareInput" onclick="focusTagInput('related_softwareInput')">
+                                    <input type="text" placeholder="Type software..." onkeydown="handleTagInput(event, 'related_software')" oninput="showAutocomplete(event, 'related_software')">
+                                </div>
+                                <div class="autocomplete-dropdown" id="related_softwareAutocomplete"></div>
+                            </div>
+                        </div>
+
+                        <div class="form-group">
+                            <label>
+                                <input type="checkbox" id="knowledgebase" class="form-checkbox">
+                                Has Knowledgebase
+                            </label>
+                        </div>
+
+                        <div class="form-group" style="display: flex; gap: 10px; margin-top: 20px;">
+                            <button type="button" class="btn btn-primary" onclick="saveTool()">Save Tool</button>
+                            <button type="button" class="btn btn-secondary" onclick="clearForm()">Clear</button>
+                            <button type="button" class="btn btn-danger" onclick="deleteTool()" id="deleteBtn" style="display: none;">Delete</button>
+                        </div>
+                    </form>
+                </div>
+
+                <!-- Domain-Agnostic Categories Section -->
+                <div id="categories-section" class="section-content">
+                    <h2>Domain-Agnostic Software Categories</h2>
+                    <div class="categories-list" id="categoriesList"></div>
+                    <form id="categoryForm">
+                        <div class="form-group">
+                            <label class="form-label">Category ID *</label>
+                            <input type="text" id="categoryId" class="form-input" required placeholder="category-id">
+                        </div>
+                        <div class="form-group">
+                            <label class="form-label">Category Name *</label>
+                            <input type="text" id="categoryName" class="form-input" required placeholder="Category Name">
+                        </div>
+                        <div class="form-group">
+                            <label class="form-label">Description</label>
+                            <textarea id="categoryDescription" class="form-textarea" placeholder="Category description..."></textarea>
+                        </div>
+                        <div class="form-group">
+                            <label class="form-label">Use Cases</label>
+                            <div class="tag-input-container">
+                                <div class="tag-input" id="useCasesInput" onclick="focusTagInput('useCasesInput')">
+                                    <input type="text" placeholder="Type use case..." onkeydown="handleTagInput(event, 'useCases')">
+                                </div>
+                            </div>
+                        </div>
+                        <div class="form-group" style="display: flex; gap: 10px;">
+                            <button type="button" class="btn btn-primary" onclick="saveCategory()">Save Category</button>
+                            <button type="button" class="btn btn-secondary" onclick="clearCategoryForm()">Clear</button>
+                            <button type="button" class="btn btn-danger" onclick="deleteCategory()" id="deleteCategoryBtn" style="display: none;">Delete</button>
+                        </div>
+                    </form>
+                </div>
+
+                <!-- Other sections placeholder -->
+                <div id="domains-section" class="section-content">
+                    <h2>Domains Management</h2>
+                    <p>Domain management interface would go here...</p>
+                </div>
+
+                <div id="phases-section" class="section-content">
+                    <h2>Phases Management</h2>
+                    <p>Phases management interface would go here...</p>
+                </div>
+
+                <div id="scenarios-section" class="section-content">
+                    <h2>Scenarios Management</h2>
+                    <p>Scenarios management interface would go here...</p>
+                </div>
+            </section>
+        </div>
+    </div>
+
+    <div class="status-bar">
+        <div id="statusText">Ready</div>
+        <div style="font-size: 0.8em; color: var(--text-secondary);">
+            Tools.yaml Editor v1.1 | 
+            Shortcuts: Ctrl+S (Save), Ctrl+N (New), Ctrl+E (Export), Alt+V (Validate), Ctrl+F (Search), Esc (Clear)
+        </div>
+    </div>
+
+    <script>
+        // Global data storage
+        let toolsData = {
+            tools: [],
+            domains: [],
+            phases: [],
+            'domain-agnostic-software': [],
+            scenarios: [],
+            skill_levels: {}
+        };
+        
+        let filteredTools = [];
+        let selectedTool = null;
+        let editingIndex = -1;
+        let selectedCategory = null;
+        let editingCategoryIndex = -1;
+        let currentSection = 'tools';
+
+        // Initialize the application
+        function init() {
+            const savedTheme = localStorage.getItem('tools-editor-theme') || 'dark';
+            document.body.setAttribute('data-theme', savedTheme);
+            const themeToggle = document.querySelector('.theme-toggle');
+            themeToggle.textContent = savedTheme === 'light' ? '🌙' : '☀️';
+
+            // Load sample data if none exists
+            if (toolsData.domains.length === 0) {
+                toolsData.domains = [
+                    { id: 'incident-response', name: 'Incident Response' },
+                    { id: 'static-investigations', name: 'Static Investigations' },
+                    { id: 'malware-analysis', name: 'Malware Analysis' },
+                    { id: 'network-forensics', name: 'Network Forensics' },
+                    { id: 'mobile-forensics', name: 'Mobile Forensics' },
+                    { id: 'cloud-forensics', name: 'Cloud Forensics' }
+                ];
+            }
+
+            if (toolsData.phases.length === 0) {
+                toolsData.phases = [
+                    { id: 'data-collection', name: 'Data Collection' },
+                    { id: 'examination', name: 'Examination' },
+                    { id: 'analysis', name: 'Analysis' },
+                    { id: 'reporting', name: 'Reporting' }
+                ];
+            }
+
+            if (toolsData['domain-agnostic-software'].length === 0) {
+                toolsData['domain-agnostic-software'] = [
+                    { id: 'data-recovery', name: 'Data Recovery', description: 'Tools for recovering deleted or corrupted data' },
+                    { id: 'file-analysis', name: 'File Analysis', description: 'Tools for analyzing file contents and metadata' },
+                    { id: 'documentation', name: 'Documentation', description: 'Tools for creating reports and documentation' }
+                ];
+            }
+
+            populateFilters();
+
+            // ✅ Initialize filteredTools to everything at startup
+            filteredTools = Array.isArray(toolsData.tools) ? toolsData.tools.slice() : [];
+            renderTools();
+            renderCategories();
+            updateStatus('Application initialized');
+        }
+
+
+        // Section switching
+        function switchSection(section) {
+            currentSection = section;
+            document.querySelectorAll('.section-tab').forEach(tab => tab.classList.remove('active'));
+            document.querySelectorAll('.section-content').forEach(content => content.classList.remove('active'));
+            
+            document.querySelector(`[onclick="switchSection('${section}')"]`).classList.add('active');
+            document.getElementById(`${section}-section`).classList.add('active');
+            
+            if (section === 'categories') {
+                renderCategories();
+            }
+        }
+
+        // Theme management
+        function toggleTheme() {
+            const body = document.body;
+            const currentTheme = body.getAttribute('data-theme');
+            const newTheme = currentTheme === 'light' ? 'dark' : 'light';
+            body.setAttribute('data-theme', newTheme);
+            
+            localStorage.setItem('tools-editor-theme', newTheme);
+            
+            const themeToggle = document.querySelector('.theme-toggle');
+            themeToggle.textContent = newTheme === 'light' ? '🌙' : '☀️';
+            
+            updateStatus(`Switched to ${newTheme} theme`);
+        }
+
+        // Status updates
+        function updateStatus(message) {
+            document.getElementById('statusText').textContent = message;
+        }
+
+        // Populate filter dropdowns
+        function populateFilters() {
+            const domainFilter = document.getElementById('domainFilter');
+            domainFilter.innerHTML = '<option value="">All Domains</option>';
+            toolsData.domains.forEach(domain => {
+                const option = document.createElement('option');
+                option.value = domain.id;
+                option.textContent = domain.name;
+                domainFilter.appendChild(option);
+            });
+        }
+
+        // Get all unique values for autocomplete
+        function getUniqueValues(field) {
+            const values = new Set();
+            toolsData.tools.forEach(tool => {
+                if (tool[field]) {
+                    if (Array.isArray(tool[field])) {
+                        tool[field].forEach(value => values.add(value));
+                    } else {
+                        values.add(tool[field]);
+                    }
+                }
+            });
+            
+            // Add predefined values
+            if (field === 'domains') {
+                toolsData.domains.forEach(domain => values.add(domain.id));
+            } else if (field === 'phases') {
+                toolsData.phases.forEach(phase => values.add(phase.id));
+            } else if (field === 'platforms') {
+                ['Windows', 'macOS', 'Linux', 'Web', 'Mobile', 'Cross-platform'].forEach(p => values.add(p));
+            } else if (field === 'domain-agnostic-software') {
+                toolsData['domain-agnostic-software'].forEach(cat => values.add(cat.id));
+            } else if (field === 'related_concepts') {
+                toolsData.tools.filter(t => t.type === 'concept').forEach(t => values.add(t.name));
+            } else if (field === 'related_software') {
+                toolsData.tools.filter(t => t.type === 'software').forEach(t => values.add(t.name));
+            }
+            
+            return Array.from(values).sort();
+        }
+
+        // Tag input handling - consolidated function
+        function focusTagInput(containerId) {
+            const input = document.querySelector(`#${containerId} input`);
+            input.focus();
+        }
+
+        function handleTagInput(event, field) {
+            const dropdown = document.getElementById(`${field}Autocomplete`);
+            const items = dropdown ? dropdown.querySelectorAll('.autocomplete-item') : [];
+            let selectedIndex = Array.from(items).findIndex(item => item.classList.contains('selected'));
+            
+            if (event.key === 'ArrowDown') {
+                event.preventDefault();
+                selectedIndex = Math.min(selectedIndex + 1, items.length - 1);
+                updateAutocompleteSelection(items, selectedIndex);
+            } else if (event.key === 'ArrowUp') {
+                event.preventDefault();
+                selectedIndex = Math.max(selectedIndex - 1, 0);
+                updateAutocompleteSelection(items, selectedIndex);
+            } else if (event.key === 'Enter') {
+                event.preventDefault();
+                if (selectedIndex >= 0 && items[selectedIndex]) {
+                    const value = items[selectedIndex].textContent;
+                    addTag(field, value);
+                    event.target.value = '';
+                    hideAutocomplete(field);
+                } else {
+                    const value = event.target.value.trim();
+                    if (value) {
+                        addTag(field, value);
+                        event.target.value = '';
+                        hideAutocomplete(field);
+                    }
+                }
+            } else if (event.key === ',' || event.key === 'Tab') {
+                event.preventDefault();
+                const input = event.target;
+                const value = input.value.trim();
+                if (value) {
+                    addTag(field, value);
+                    input.value = '';
+                    hideAutocomplete(field);
+                }
+            } else if (event.key === 'Backspace' && event.target.value === '') {
+                removeLastTag(field);
+            }
+        }
+        
+        function updateAutocompleteSelection(items, selectedIndex) {
+            items.forEach((item, index) => {
+                item.classList.toggle('selected', index === selectedIndex);
+            });
+        }
+
+        function addTag(field, value) {
+            const container = document.getElementById(`${field}Input`);
+            const input = container.querySelector('input');
+            
+            // Check if tag already exists
+            const existingTags = getTagValues(field);
+            if (existingTags.includes(value)) return;
+            
+            const tagElement = document.createElement('div');
+            tagElement.className = 'tag-input-item';
+            tagElement.innerHTML = `${value} <span class="remove" onclick="removeTag('${field}', this)">×</span>`;
+            
+            container.insertBefore(tagElement, input);
+        }
+
+        function removeTag(field, element) {
+            element.parentElement.remove();
+        }
+
+        function removeLastTag(field) {
+            const container = document.getElementById(`${field}Input`);
+            const tags = container.querySelectorAll('.tag-input-item');
+            if (tags.length > 0) {
+                tags[tags.length - 1].remove();
+            }
+        }
+
+        function getTagValues(field) {
+            const container = document.getElementById(`${field}Input`);
+            if (!container) return [];
+            return Array.from(container.querySelectorAll('.tag-input-item')).map(item => 
+                item.textContent.replace('×', '').trim()
+            );
+        }
+
+        function setTagValues(field, values) {
+            const container = document.getElementById(`${field}Input`);
+            if (!container) return;
+            // Clear existing tags
+            container.querySelectorAll('.tag-input-item').forEach(item => item.remove());
+            // Add new tags
+            if (values && Array.isArray(values)) {
+                values.forEach(value => addTag(field, value));
+            }
+        }
+
+        // Autocomplete functionality
+        function showAutocomplete(event, field) {
+            const input = event.target;
+            const value = input.value.toLowerCase();
+            const dropdown = document.getElementById(`${field}Autocomplete`);
+            
+            if (!dropdown || value.length < 1) {
+                hideAutocomplete(field);
+                return;
+            }
+            
+            const suggestions = getUniqueValues(field).filter(item => 
+                item.toLowerCase().includes(value)
+            );
+            
+            if (suggestions.length === 0) {
+                hideAutocomplete(field);
+                return;
+            }
+            
+            dropdown.innerHTML = '';
+            suggestions.slice(0, 10).forEach((suggestion, index) => {
+                const item = document.createElement('div');
+                item.className = 'autocomplete-item';
+                if (index === 0) item.classList.add('selected');
+                item.textContent = suggestion;
+                item.onclick = () => {
+                    addTag(field, suggestion);
+                    input.value = '';
+                    hideAutocomplete(field);
+                };
+                dropdown.appendChild(item);
+            });
+            
+            dropdown.style.display = 'block';
+        }
+
+        function hideAutocomplete(field) {
+            const dropdown = document.getElementById(`${field}Autocomplete`);
+            if (dropdown) dropdown.style.display = 'none';
+        }
+
+        // Consolidated validation function
+        function validateTool(tool, index = null) {
+            const prefix = index !== null ? `Tool ${index + 1} (${tool.name || 'Unnamed'}): ` : '';
+            const errors = [];
+            
+            // Required fields validation
+            if (!tool.name || typeof tool.name !== 'string' || tool.name.trim() === '') {
+                errors.push(prefix + 'Name is required and must be a non-empty string');
+            }
+            
+            if (!tool.type || !['software', 'method', 'concept'].includes(tool.type)) {
+                errors.push(prefix + 'Type is required and must be one of: software, method, concept');
+            }
+            
+            if (!tool.description || typeof tool.description !== 'string' || tool.description.trim() === '') {
+                errors.push(prefix + 'Description is required and must be a non-empty string');
+            }
+            
+            if (!tool.url || typeof tool.url !== 'string' || tool.url.trim() === '') {
+                errors.push(prefix + 'URL is required and must be a non-empty string');
+            }
+            
+            if (!tool.skillLevel || !['novice', 'beginner', 'intermediate', 'advanced', 'expert'].includes(tool.skillLevel)) {
+                errors.push(prefix + 'Skill level is required and must be one of: novice, beginner, intermediate, advanced, expert');
+            }
+            
+            // URL format validation
+            if (tool.url && tool.url.trim() !== '') {
+                try {
+                    new URL(tool.url);
+                } catch {
+                    errors.push(prefix + 'URL must be a valid URL format');
+                }
+            }
+            
+            // Project URL validation (if provided)
+            if (tool.projectUrl && tool.projectUrl.trim() !== '') {
+                try {
+                    new URL(tool.projectUrl);
+                } catch {
+                    errors.push(prefix + 'Project URL must be a valid URL format');
+                }
+            }
+            
+            // Status URL validation (if provided)
+            if (tool.statusUrl && tool.statusUrl.trim() !== '') {
+                try {
+                    new URL(tool.statusUrl);
+                } catch {
+                    errors.push(prefix + 'Status URL must be a valid URL format');
+                }
+            }
+            
+            // Array fields validation
+            const arrayFields = ['domains', 'phases', 'platforms', 'tags', 'related_concepts', 'related_software', 'domain-agnostic-software'];
+            arrayFields.forEach(field => {
+                if (tool[field] !== undefined && tool[field] !== null && !Array.isArray(tool[field])) {
+                    errors.push(prefix + `${field} must be an array if provided`);
+                }
+            });
+            
+            // String fields validation (optional but must be strings if provided)
+            const stringFields = ['icon', 'license', 'accessType', 'statusUrl'];
+            stringFields.forEach(field => {
+                if (tool[field] !== undefined && tool[field] !== null && typeof tool[field] !== 'string') {
+                    errors.push(prefix + `${field} must be a string if provided`);
+                }
+            });
+            
+            // Boolean field validation
+            if (tool.knowledgebase !== undefined && tool.knowledgebase !== null && typeof tool.knowledgebase !== 'boolean') {
+                errors.push(prefix + 'knowledgebase must be a boolean if provided');
+            }
+            
+            // Duplicate name check: only when we know which index we're validating
+            if (index !== null && tool.name) {
+                const clashIndex = toolsData.tools.findIndex((t, i) => i !== index && t.name === tool.name);
+                if (clashIndex >= 0) {
+                    errors.push(prefix + `Duplicate name "${tool.name}" also found at index ${clashIndex}`);
+                }
+            }
+
+            
+            return errors;
+        }
+
+        // Tool management
+        function addNewTool() {
+            switchSection('tools');
+            clearForm();
+            editingIndex = -1;
+            document.getElementById('editTitle').textContent = 'Add New Tool';
+            document.getElementById('deleteBtn').style.display = 'none';
+            updateStatus('Creating new tool');
+        }
+
+        function editTool(index) {
+            switchSection('tools');
+            const tool = filteredTools[index];
+            editingIndex = toolsData.tools.indexOf(tool);
+            
+            // Populate form with all fields
+            document.getElementById('name').value = tool.name || '';
+            document.getElementById('type').value = tool.type || '';
+            document.getElementById('icon').value = tool.icon || '';
+            document.getElementById('description').value = tool.description || '';
+            document.getElementById('url').value = tool.url || '';
+            document.getElementById('projectUrl').value = tool.projectUrl || '';
+            document.getElementById('statusUrl').value = tool.statusUrl || '';
+            document.getElementById('skillLevel').value = tool.skillLevel || '';
+            document.getElementById('license').value = tool.license || '';
+            document.getElementById('accessType').value = tool.accessType || '';
+            document.getElementById('knowledgebase').checked = tool.knowledgebase || false;
+            
+            // Set tag values for all tag fields
+            setTagValues('domains', tool.domains);
+            setTagValues('phases', tool.phases);
+            setTagValues('platforms', tool.platforms);
+            setTagValues('tags', tool.tags);
+            setTagValues('related_concepts', tool.related_concepts);
+            setTagValues('related_software', tool.related_software);
+            setTagValues('domain-agnostic-software', tool['domain-agnostic-software']);
+            
+            document.getElementById('editTitle').textContent = `Edit: ${tool.name}`;
+            document.getElementById('deleteBtn').style.display = 'inline-block';
+            
+            updateStatus(`Editing ${tool.name}`);
+        }
+
+        function saveTool() {
+            const form = document.getElementById('toolForm');
+            if (!form.checkValidity()) {
+                form.reportValidity();
+                return;
+            }
+            
+            const tool = {
+                name: document.getElementById('name').value.trim(),
+                type: document.getElementById('type').value,
+                description: document.getElementById('description').value.trim(),
+                url: document.getElementById('url').value.trim(),
+                skillLevel: document.getElementById('skillLevel').value,
+                domains: getTagValues('domains'),
+                phases: getTagValues('phases'),
+                platforms: getTagValues('platforms'),
+                tags: getTagValues('tags'),
+                related_concepts: getTagValues('related_concepts'),
+                related_software: getTagValues('related_software')
+            };
+            
+            // Add domain-agnostic-software field
+            const domainAgnosticCategories = getTagValues('domain-agnostic-software');
+            if (domainAgnosticCategories.length > 0) {
+                tool['domain-agnostic-software'] = domainAgnosticCategories;
+            }
+            
+            // Add optional fields only if they have values
+            const optionalFields = ['icon', 'projectUrl', 'statusUrl', 'license', 'accessType'];
+            optionalFields.forEach(field => {
+                const value = document.getElementById(field).value.trim();
+                if (value) tool[field] = value;
+            });
+            
+            if (document.getElementById('knowledgebase').checked) {
+                tool.knowledgebase = true;
+            }
+            
+            // Remove empty arrays to keep YAML clean
+            Object.keys(tool).forEach(key => {
+                if (Array.isArray(tool[key]) && tool[key].length === 0) {
+                    delete tool[key];
+                }
+            });
+            
+            // Validate tool
+            const errors = validateTool(tool);
+            if (errors.length > 0) {
+                showValidationErrors(errors);
+                return;
+            }
+            
+            hideValidationErrors();
+            
+            const isNewTool = editingIndex < 0;
+            const toolName = tool.name;
+            
+            if (editingIndex >= 0) {
+                toolsData.tools[editingIndex] = tool;
+                updateStatus(`✅ Updated ${toolName}`);
+                showSaveSuccess('Tool updated successfully!');
+            } else {
+                toolsData.tools.push(tool);
+                updateStatus(`✅ Added ${toolName}`);
+                showSaveSuccess('Tool added successfully!');
+            }
+            
+            filterTools();
+            clearForm();
+            
+            // Update validation summary if needed
+            setTimeout(() => {
+                const validationFilter = document.getElementById('validationFilter').value;
+                if (validationFilter === 'invalid') {
+                    const remainingErrors = [];
+                    toolsData.tools.forEach((t, index) => {
+                        const toolErrors = validateTool(t, index);
+                        remainingErrors.push(...toolErrors);
+                    });
+                    
+                    if (remainingErrors.length === 0) {
+                        hideValidationErrors();
+                        updateStatus('🎉 All validation errors fixed!');
+                        document.getElementById('validationFilter').value = '';
+                        filterTools();
+                    } else {
+                        const validationSummary = document.getElementById('validationSummary');
+                        const validationCount = document.getElementById('validationCount');
+                        if (validationSummary && validationCount) {
+                            validationCount.textContent = toolsData.tools.filter(t => validateTool(t, toolsData.tools.indexOf(t)).length > 0).length;
+                            validationSummary.style.display = validationCount.textContent === '0' ? 'none' : 'block';
+                        }
+                    }
+                }
+            }, 100);
+        }
+
+        function showSaveSuccess(message) {
+            let successDiv = document.getElementById('saveSuccess');
+            if (!successDiv) {
+                successDiv = document.createElement('div');
+                successDiv.id = 'saveSuccess';
+                successDiv.style.cssText = `
+                    position: fixed;
+                    top: 20px;
+                    right: 20px;
+                    background: var(--success-color);
+                    color: white;
+                    padding: 12px 20px;
+                    border-radius: 6px;
+                    box-shadow: var(--shadow);
+                    z-index: 1000;
+                    opacity: 0;
+                    transform: translateY(-20px);
+                    transition: all 0.3s ease;
+                `;
+                document.body.appendChild(successDiv);
+            }
+            
+            successDiv.textContent = message;
+            
+            requestAnimationFrame(() => {
+                successDiv.style.opacity = '1';
+                successDiv.style.transform = 'translateY(0)';
+            });
+            
+            setTimeout(() => {
+                successDiv.style.opacity = '0';
+                successDiv.style.transform = 'translateY(-20px)';
+                
+                setTimeout(() => {
+                    if (successDiv.parentNode) {
+                        successDiv.parentNode.removeChild(successDiv);
+                    }
+                }, 300);
+            }, 3000);
+        }
+
+        function deleteTool() {
+            if (editingIndex >= 0 && confirm('Are you sure you want to delete this tool?')) {
+                const toolName = toolsData.tools[editingIndex].name;
+                toolsData.tools.splice(editingIndex, 1);
+                updateStatus(`Deleted ${toolName}`);
+                renderTools();
+                clearForm();
+            }
+        }
+
+        function clearForm() {
+            document.getElementById('toolForm').reset();
+            const tagFields = ['domains', 'phases', 'platforms', 'tags', 'related_concepts', 'related_software', 'domain-agnostic-software'];
+            tagFields.forEach(field => {
+                setTagValues(field, []);
+            });
+            editingIndex = -1;
+            document.getElementById('editTitle').textContent = 'Tool Editor';
+            document.getElementById('deleteBtn').style.display = 'none';
+        }
+
+        // Category management
+        function renderCategories() {
+            const container = document.getElementById('categoriesList');
+            if (!container) return;
+            
+            if (!toolsData['domain-agnostic-software'] || toolsData['domain-agnostic-software'].length === 0) {
+                container.innerHTML = '<div style="text-align: center; padding: 40px; color: var(--text-secondary);">No categories found</div>';
+                return;
+            }
+            
+            container.innerHTML = toolsData['domain-agnostic-software'].map((category, index) => `
+                <div class="category-item ${selectedCategory === category ? 'selected' : ''}" onclick="selectCategory(${index})">
+                    <div class="category-name">${category.name}</div>
+                    <div class="category-description">${category.description || 'No description'}</div>
+                    <div style="font-size: 0.8em; color: var(--text-secondary); margin-top: 4px;">
+                        ID: ${category.id} ${category.use_cases ? `• ${category.use_cases.length} use cases` : ''}
+                    </div>
+                </div>
+            `).join('');
+        }
+
+        function selectCategory(index) {
+            selectedCategory = toolsData['domain-agnostic-software'][index];
+            editingCategoryIndex = index;
+            
+            // Populate form
+            document.getElementById('categoryId').value = selectedCategory.id || '';
+            document.getElementById('categoryName').value = selectedCategory.name || '';
+            document.getElementById('categoryDescription').value = selectedCategory.description || '';
+            setTagValues('useCases', selectedCategory.use_cases);
+            
+            document.getElementById('deleteCategoryBtn').style.display = 'inline-block';
+            renderCategories();
+        }
+
+        function saveCategory() {
+            const category = {
+                id: document.getElementById('categoryId').value.trim(),
+                name: document.getElementById('categoryName').value.trim(),
+                description: document.getElementById('categoryDescription').value.trim(),
+                use_cases: getTagValues('useCases')
+            };
+            
+            if (!category.id || !category.name) {
+                alert('Category ID and Name are required');
+                return;
+            }
+            
+            // Remove empty description
+            if (!category.description) {
+                delete category.description;
+            }
+            
+            // Remove empty use_cases array
+            if (category.use_cases.length === 0) {
+                delete category.use_cases;
+            }
+            
+            if (editingCategoryIndex >= 0) {
+                toolsData['domain-agnostic-software'][editingCategoryIndex] = category;
+                updateStatus(`✅ Updated category ${category.name}`);
+            } else {
+                toolsData['domain-agnostic-software'].push(category);
+                updateStatus(`✅ Added category ${category.name}`);
+            }
+            
+            renderCategories();
+            clearCategoryForm();
+        }
+
+        function deleteCategory() {
+            if (editingCategoryIndex >= 0 && confirm('Are you sure you want to delete this category?')) {
+                const categoryName = toolsData['domain-agnostic-software'][editingCategoryIndex].name;
+                toolsData['domain-agnostic-software'].splice(editingCategoryIndex, 1);
+                updateStatus(`Deleted category ${categoryName}`);
+                renderCategories();
+                clearCategoryForm();
+            }
+        }
+
+        function clearCategoryForm() {
+            document.getElementById('categoryForm').reset();
+            setTagValues('useCases', []);
+            selectedCategory = null;
+            editingCategoryIndex = -1;
+            document.getElementById('deleteCategoryBtn').style.display = 'none';
+            renderCategories();
+        }
+
+        function validateAllTools() {
+            const allErrors = [];
+            
+            toolsData.tools.forEach((tool, index) => {
+                const toolErrors = validateTool(tool, index);
+                allErrors.push(...toolErrors);
+            });
+            
+            // Check for duplicate names across all tools
+            const nameMap = new Map();
+            toolsData.tools.forEach((tool, index) => {
+                if (tool.name) {
+                    if (nameMap.has(tool.name)) {
+                        allErrors.push(`Duplicate name "${tool.name}" found at indices ${nameMap.get(tool.name)} and ${index}`);
+                    } else {
+                        nameMap.set(tool.name, index);
+                    }
+                }
+            });
+            
+            if (allErrors.length === 0) {
+                updateStatus('✅ All tools are valid');
+                hideValidationErrors();
+            } else {
+                updateStatus(`❌ Found ${allErrors.length} validation errors`);
+                showValidationErrors(allErrors);
+                document.getElementById('validationErrors').scrollIntoView({ behavior: 'smooth' });
+            }
+            
+            return allErrors.length === 0;
+        }
+
+        function showValidationErrors(errors) {
+            const errorDiv = document.getElementById('validationErrors');
+            
+            if (Array.isArray(errors) && errors.length > 10) {
+                const firstTen = errors.slice(0, 10);
+                const remaining = errors.length - 10;
+                errorDiv.innerHTML = `
+                    <strong>Validation Errors (showing first 10 of ${errors.length}):</strong><br>
+                    ${firstTen.map(error => `• ${error}`).join('<br>')}
+                    <br><strong>... and ${remaining} more errors</strong>
+                `;
+            } else {
+                errorDiv.innerHTML = `
+                    <strong>Validation Errors:</strong><br>
+                    ${(Array.isArray(errors) ? errors : [errors]).map(error => `• ${error}`).join('<br>')}
+                `;
+            }
+            
+            errorDiv.style.display = 'block';
+        }
+
+        function hideValidationErrors() {
+            document.getElementById('validationErrors').style.display = 'none';
+        }
+
+        // Filtering and sorting
+        function filterTools() {
+            const rawSearch = document.getElementById('searchInput').value || '';
+            const searchTerm = rawSearch.toLowerCase();
+            const typeFilter = document.getElementById('typeFilter').value;
+            const skillFilter = document.getElementById('skillFilter').value;
+            const domainFilter = document.getElementById('domainFilter').value;
+            const knowledgebaseFilter = document.getElementById('knowledgebaseFilter').value;
+            const validationFilter = document.getElementById('validationFilter').value;
+
+            filteredTools = toolsData.tools.filter(tool => {
+                // Safe access helpers
+                const name = (tool.name || '').toLowerCase();
+                const desc = (tool.description || '').toLowerCase();
+                const tagHit = Array.isArray(tool.tags) && tool.tags.some(tag => (tag || '').toLowerCase().includes(searchTerm));
+
+                const matchesSearch = !searchTerm || name.includes(searchTerm) || desc.includes(searchTerm) || tagHit;
+                const matchesType = !typeFilter || tool.type === typeFilter;
+                const matchesSkill = !skillFilter || tool.skillLevel === skillFilter;
+                const matchesDomain = !domainFilter || (Array.isArray(tool.domains) && tool.domains.includes(domainFilter));
+                const matchesKnowledgebase = !knowledgebaseFilter ||
+                    (knowledgebaseFilter === 'true' && !!tool.knowledgebase) ||
+                    (knowledgebaseFilter === 'false' && !tool.knowledgebase);
+
+                let matchesValidation = true;
+                if (validationFilter) {
+                    const hasValidationIssues = validateTool(tool, toolsData.tools.indexOf(tool)).length > 0;
+                    matchesValidation = validationFilter === 'invalid' ? hasValidationIssues : !hasValidationIssues;
+                }
+
+                return matchesSearch && matchesType && matchesSkill && matchesDomain && matchesKnowledgebase && matchesValidation;
+            });
+
+            sortTools();
+        }
+
+
+        function showInvalidToolsFilter(errorCount) {
+            document.getElementById('validationFilter').value = 'invalid';
+            filterTools();
+            
+            showValidationErrors([
+                `⚠️ Import completed with ${errorCount} validation errors.`,
+                '',
+                'The filter has been set to show only tools with validation issues.',
+                'Please fix these errors before exporting to ensure compatibility with the web application.',
+                '',
+                'Click on each tool marked with ⚠️ to edit and resolve the issues.'
+            ]);
+            
+            updateStatus(`⚠️ Import completed - ${errorCount} tools need fixing`);
+        }
+
+        function clearAllFilters() {
+            document.getElementById('searchInput').value = '';
+            document.getElementById('typeFilter').value = '';
+            document.getElementById('skillFilter').value = '';
+            document.getElementById('domainFilter').value = '';
+            document.getElementById('knowledgebaseFilter').value = '';
+            document.getElementById('validationFilter').value = '';
+            document.getElementById('sortBy').value = 'name';
+            filterTools();
+            updateStatus('All filters cleared');
+        }
+
+        function sortTools() {
+            const sortBy = document.getElementById('sortBy').value;
+
+            filteredTools.sort((a, b) => {
+                let aVal = a && a[sortBy];
+                let bVal = b && b[sortBy];
+
+                if (Array.isArray(aVal)) aVal = aVal.join(', ');
+                if (Array.isArray(bVal)) bVal = bVal.join(', ');
+
+                aVal = (aVal == null ? '' : String(aVal));
+                bVal = (bVal == null ? '' : String(bVal));
+
+                return aVal.localeCompare(bVal);
+            });
+
+            renderTools();
+        }
+
+        // Rendering
+        function renderTools() {
+            const container = document.getElementById('toolsList');
+            const countElement = document.getElementById('toolCount');
+
+            if (!Array.isArray(filteredTools) || filteredTools.length === 0) {
+                countElement.textContent = 0;
+                container.innerHTML = '<div style="text-align: center; padding: 40px; color: var(--text-secondary);">No tools found</div>';
+                return;
+            }
+            
+            countElement.textContent = filteredTools.length;
+            
+            container.innerHTML = filteredTools.map((tool, index) => {
+                const hasValidationIssues = validateTool(tool, toolsData.tools.indexOf(tool)).length > 0;
+                
+                return `
+                <div class="tool-item ${selectedTool === tool ? 'selected' : ''}" onclick="selectTool(${index})">
+                    <div class="tool-header">
+                        <div>
+                            <div class="tool-name">
+                                ${hasValidationIssues ? '⚠️ ' : ''}
+                                ${tool.icon || '🔧'} ${tool.name || 'Unnamed Tool'}
+                                ${tool.knowledgebase ? ' 📖' : ''}
+                                ${tool.projectUrl ? ' 🌐' : ''}
+                            </div>
+                            <div class="tool-meta">
+                                <span>${tool.type || 'Unknown'}</span>
+                                <span>${tool.skillLevel || 'Unknown'}</span>
+                                ${tool.license ? `<span>${tool.license}</span>` : ''}
+                                ${tool.accessType ? `<span>${tool.accessType}</span>` : ''}
+                                ${hasValidationIssues ? '<span style="color: var(--danger-color);">⚠️ Issues</span>' : ''}
+                            </div>
+                        </div>
+                        <button class="btn btn-secondary btn-sm" onclick="event.stopPropagation(); editTool(${index})">Edit</button>
+                    </div>
+                    <div class="tool-description">${tool.description || 'No description provided'}</div>
+                    ${tool.domains && tool.domains.length ? `
+                        <div style="margin-top: 8px; font-size: 0.85em; color: var(--text-secondary);">
+                            <strong>Domains:</strong> ${tool.domains.slice(0, 3).join(', ')}${tool.domains.length > 3 ? '...' : ''}
+                        </div>
+                    ` : ''}
+                    ${tool['domain-agnostic-software'] && tool['domain-agnostic-software'].length ? `
+                        <div style="margin-top: 4px; font-size: 0.85em; color: var(--text-secondary);">
+                            <strong>Categories:</strong> ${tool['domain-agnostic-software'].slice(0, 2).join(', ')}${tool['domain-agnostic-software'].length > 2 ? '...' : ''}
+                        </div>
+                    ` : ''}
+                    ${tool.tags ? `
+                        <div class="tags">
+                            ${tool.tags.slice(0, 5).map(tag => `<span class="tag">${tag}</span>`).join('')}
+                            ${tool.tags.length > 5 ? `<span class="tag">+${tool.tags.length - 5}</span>` : ''}
+                        </div>
+                    ` : ''}
+                </div>
+            `}).join('');
+        }
+
+        function selectTool(index) {
+            selectedTool = filteredTools[index];
+            renderTools();
+        }
+
+        function clearSelection() {
+            selectedTool = null;
+            renderTools();
+        }
+
+        // Statistics
+        function showStats() {
+            const stats = calculateStats();
+            const container = document.getElementById('statsContainer');
+            
+            container.innerHTML = Object.entries(stats).map(([key, value]) => `
+                <div class="stat-item">
+                    <div class="stat-number">${value}</div>
+                    <div>${key}</div>
+                </div>
+            `).join('');
+            
+            container.style.display = container.style.display === 'none' ? 'grid' : 'none';
+        }
+
+        function calculateStats() {
+            const stats = {
+                'Total Tools': toolsData.tools.length,
+                'Software': toolsData.tools.filter(t => t.type === 'software').length,
+                'Methods': toolsData.tools.filter(t => t.type === 'method').length,
+                'Concepts': toolsData.tools.filter(t => t.type === 'concept').length,
+                'With KB': toolsData.tools.filter(t => t.knowledgebase).length,
+                'Open Source': toolsData.tools.filter(t => t.license && t.license !== 'Proprietary').length,
+                'Categories': toolsData['domain-agnostic-software'].length
+            };
+            
+            return stats;
+        }
+
+        // Bulk operations
+        function toggleBulkPanel() {
+            const panel = document.getElementById('bulkPanel');
+            panel.classList.toggle('active');
+        }
+
+        function bulkRemoveTags() {
+            const tags = document.getElementById('bulkRemoveTagsInput').value
+                .split(',').map(t => t.trim()).filter(t => t);
+            
+            if (tags.length === 0) {
+                updateStatus('Please enter tags to remove');
+                return;
+            }
+            
+            if (!confirm(`Remove tags "${tags.join(', ')}" from all tools?`)) return;
+            
+            let count = 0;
+            toolsData.tools.forEach(tool => {
+                if (tool.tags) {
+                    const before = tool.tags.length;
+                    tool.tags = tool.tags.filter(tag => !tags.includes(tag));
+                    if (tool.tags.length < before) count++;
+                }
+            });
+            
+            updateStatus(`Removed tags from ${count} tools`);
+            document.getElementById('bulkRemoveTagsInput').value = '';
+            renderTools();
+        }
+
+        function bulkRenameTags() {
+            const fromTag = document.getElementById('bulkRenameFrom').value.trim();
+            const toTag = document.getElementById('bulkRenameTo').value.trim();
+            
+            if (!fromTag || !toTag) {
+                updateStatus('Please enter both "from" and "to" tags');
+                return;
+            }
+            
+            if (!confirm(`Rename tag "${fromTag}" to "${toTag}" in all tools?`)) return;
+            
+            let count = 0;
+            toolsData.tools.forEach(tool => {
+                if (tool.tags && tool.tags.includes(fromTag)) {
+                    const index = tool.tags.indexOf(fromTag);
+                    tool.tags[index] = toTag;
+                    count++;
+                }
+            });
+            
+            updateStatus(`Renamed tag in ${count} tools`);
+            document.getElementById('bulkRenameFrom').value = '';
+            document.getElementById('bulkRenameTo').value = '';
+            renderTools();
+        }
+
+        function bulkAssignRelatedConcepts() {
+            const concepts = document.getElementById('bulkConceptsInput').value
+                .split(',').map(t => t.trim()).filter(t => t);
+            
+            if (concepts.length === 0) {
+                updateStatus('Please enter concepts to assign');
+                return;
+            }
+            
+            if (!confirm(`Assign concepts "${concepts.join(', ')}" to all tools?`)) return;
+            
+            const selectedTools = getSelectedTools();
+            selectedTools.forEach(tool => {
+                if (!tool.related_concepts) tool.related_concepts = [];
+                concepts.forEach(concept => {
+                    if (!tool.related_concepts.includes(concept)) {
+                        tool.related_concepts.push(concept);
+                    }
+                });
+            });
+            
+            updateStatus(`Assigned concepts to ${selectedTools.length} tools`);
+            document.getElementById('bulkConceptsInput').value = '';
+            renderTools();
+        }
+
+        function bulkAssignRelatedSoftware() {
+            const software = document.getElementById('bulkSoftwareInput').value
+                .split(',').map(t => t.trim()).filter(t => t);
+            
+            if (software.length === 0) {
+                updateStatus('Please enter software to assign');
+                return;
+            }
+            
+            if (!confirm(`Assign software "${software.join(', ')}" to all tools?`)) return;
+            
+            const selectedTools = getSelectedTools();
+            selectedTools.forEach(tool => {
+                if (!tool.related_software) tool.related_software = [];
+                software.forEach(sw => {
+                    if (!tool.related_software.includes(sw)) {
+                        tool.related_software.push(sw);
+                    }
+                });
+            });
+            
+            updateStatus(`Assigned software to ${selectedTools.length} tools`);
+            document.getElementById('bulkSoftwareInput').value = '';
+            renderTools();
+        }
+
+        function getSelectedTools() {
+            return toolsData.tools;
+        }
+
+        // YAML operations
+        function importYAML(event) {
+            const file = event.target.files[0];
+            if (!file) return;
+            
+            const reader = new FileReader();
+            reader.onload = function(e) {
+                try {
+                    const yamlContent = e.target.result;
+                    const parsed = jsyaml.load(yamlContent);
+                    
+                    if (!parsed || !parsed.tools || !Array.isArray(parsed.tools)) {
+                        alert('Invalid YAML format: missing or invalid tools array');
+                        return;
+                    }
+                    
+                    const importSummary = `
+Import Summary:
+• ${parsed.tools.length} tools
+• ${parsed.tools.filter(t => t.type === 'software').length} software
+• ${parsed.tools.filter(t => t.type === 'method').length} methods  
+• ${parsed.tools.filter(t => t.type === 'concept').length} concepts
+• ${parsed.tools.filter(t => t.knowledgebase).length} with knowledgebase
+
+This will replace all current data. Continue?`;
+
+                    if (!confirm(importSummary)) {
+                        return;
+                    }
+                    
+                    toolsData = parsed;
+                    
+                    // Ensure required structure exists
+                    if (!toolsData.domains) toolsData.domains = [];
+                    if (!toolsData.phases) toolsData.phases = [];
+                    if (!toolsData['domain-agnostic-software']) toolsData['domain-agnostic-software'] = [];
+                    if (!toolsData.scenarios) toolsData.scenarios = [];
+                    if (!toolsData.skill_levels) toolsData.skill_levels = {};
+                    
+                    populateFilters();
+                    
+                    updateStatus('Validating imported data...');
+                    
+                    const validationErrors = [];
+                    toolsData.tools.forEach((tool, index) => {
+                        const toolErrors = validateTool(tool, index);
+                        validationErrors.push(...toolErrors);
+                    });
+                    
+                    // Check for duplicate names
+                    const nameMap = new Map();
+                    toolsData.tools.forEach((tool, index) => {
+                        if (tool.name) {
+                            if (nameMap.has(tool.name)) {
+                                validationErrors.push(`Duplicate name "${tool.name}" found at indices ${nameMap.get(tool.name)} and ${index}`);
+                            } else {
+                                nameMap.set(tool.name, index);
+                            }
+                        }
+                    });
+                    
+                    if (validationErrors.length > 0) {
+                        showInvalidToolsFilter(validationErrors.length); // this already calls filterTools()
+                    } else {
+                        hideValidationErrors();
+                        // ✅ show everything by default
+                        filteredTools = toolsData.tools.slice();
+                        sortTools(); // will render afterwards
+                        updateStatus(`✅ Successfully imported ${toolsData.tools.length} tools - all valid`);
+                    }
+                    
+                    renderCategories();
+                    
+                } catch (error) {
+                    alert('Error parsing YAML: ' + error.message);
+                    updateStatus('❌ YAML parsing failed');
+                }
+            };
+            reader.readAsText(file);
+        }
+
+        function exportYAML() {
+            const allErrors = [];
+            toolsData.tools.forEach((tool, index) => {
+                const toolErrors = validateTool(tool, index);
+                allErrors.push(...toolErrors);
+            });
+            
+            if (allErrors.length > 0) {
+                const confirmExport = confirm(`⚠️ Warning: Found ${allErrors.length} validation errors in the database.\n\nExporting this data may cause issues when importing into the web application.\n\nWould you like to:\n• Click OK to export anyway\n• Click Cancel to fix errors first (recommended)`);
+                
+                if (!confirmExport) {
+                    showInvalidToolsFilter(allErrors.length);
+                    return;
+                }
+            }
+            
+            try {
+                const processedData = {
+                    ...toolsData,
+                    tools: toolsData.tools.map(tool => {
+                        const processedTool = { ...tool };
+                        
+                        // Clean up empty arrays and null values
+                        Object.keys(processedTool).forEach(key => {
+                            if (Array.isArray(processedTool[key]) && processedTool[key].length === 0) {
+                                delete processedTool[key];
+                            }
+                            if (processedTool[key] === null || processedTool[key] === undefined || processedTool[key] === '') {
+                                delete processedTool[key];
+                            }
+                        });
+                        
+                        return processedTool;
+                    })
+                };
+                
+                const yamlContent = jsyaml.dump(processedData, {
+                    indent: 2,
+                    lineWidth: 80,
+                    noRefs: true,
+                    sortKeys: false,
+                    styles: {
+                        '!!str': 'fold'
+                    }
+                });
+                
+                const lines = yamlContent.split('\n');
+                const processedLines = [];
+                
+                for (let i = 0; i < lines.length; i++) {
+                    const line = lines[i];
+                    
+                    if (line.trim().startsWith('description:')) {
+                        if (line.includes('"') || line.includes("'")) {
+                            const indent = line.match(/^(\s*)/)[1];
+                            processedLines.push(`${indent}description: >-`);
+                            
+                            let descContent = line.substring(line.indexOf(':') + 1).trim();
+                            if (descContent.startsWith('"') || descContent.startsWith("'")) {
+                                descContent = descContent.slice(1, -1);
+                            }
+                            
+                            const words = descContent.split(' ');
+                            let currentLine = '';
+                            const descIndent = indent + '  ';
+                            
+                            words.forEach(word => {
+                                if (currentLine.length + word.length > 76) {
+                                    if (currentLine) {
+                                        processedLines.push(descIndent + currentLine.trim());
+                                    }
+                                    currentLine = word + ' ';
+                                } else {
+                                    currentLine += word + ' ';
+                                }
+                            });
+                            
+                            if (currentLine.trim()) {
+                                processedLines.push(descIndent + currentLine.trim());
+                            }
+                        } else {
+                            processedLines.push(line);
+                        }
+                    } else {
+                        processedLines.push(line);
+                    }
+                }
+                
+                const finalYaml = processedLines.join('\n');
+                
+                const blob = new Blob([finalYaml], { type: 'text/yaml' });
+                const url = URL.createObjectURL(blob);
+                const a = document.createElement('a');
+                a.href = url;
+                a.download = 'tools.yaml';
+                a.click();
+                URL.revokeObjectURL(url);
+                
+                updateStatus(allErrors.length > 0 ? 
+                    `⚠️ YAML exported with ${allErrors.length} validation errors` : 
+                    '✅ YAML exported successfully'
+                );
+            } catch (error) {
+                alert('Error exporting YAML: ' + error.message);
+            }
+        }
+
+        // Initialize on load
+        document.addEventListener('DOMContentLoaded', init);
+
+        // Keyboard shortcuts
+        document.addEventListener('keydown', function(event) {
+            if ((event.ctrlKey || event.metaKey) && event.key === 's') {
+                event.preventDefault();
+                if (currentSection === 'tools') {
+                    saveTool();
+                } else if (currentSection === 'categories') {
+                    saveCategory();
+                }
+            }
+            
+            if ((event.ctrlKey || event.metaKey) && event.key === 'n') {
+                event.preventDefault();
+                if (currentSection === 'tools') {
+                    addNewTool();
+                }
+            }
+            
+            if ((event.ctrlKey || event.metaKey) && event.key === 'e') {
+                event.preventDefault();
+                exportYAML();
+            }
+            
+            if (event.altKey && event.key === 'v') {
+                event.preventDefault();
+                validateAllTools();
+            }
+            
+            if (event.key === 'Escape') {
+                clearSelection();
+                if (currentSection === 'tools') {
+                    clearForm();
+                } else if (currentSection === 'categories') {
+                    clearCategoryForm();
+                }
+            }
+            
+            if ((event.ctrlKey || event.metaKey) && event.key === 'f') {
+                event.preventDefault();
+                document.getElementById('searchInput').focus();
+            }
+        });
+
+        // Hide autocomplete when clicking outside
+        document.addEventListener('click', function(event) {
+            if (!event.target.closest('.autocomplete-container')) {
+                document.querySelectorAll('.autocomplete-dropdown').forEach(dropdown => {
+                    dropdown.style.display = 'none';
+                });
+            }
+        });
+    </script>
+</body>
+</html>
\ No newline at end of file