dnscope/core/graph_manager.py

# dnsrecon-reduced/core/graph_manager.py

"""
Graph data model for DNSRecon using NetworkX.
Manages in-memory graph storage with confidence scoring and forensic metadata.
Now fully compatible with the unified ProviderResult data model.
UPDATED: Fixed certificate styling and correlation edge labeling.
"""
import re
from datetime import datetime, timezone
from enum import Enum
from typing import Dict, List, Any, Optional, Tuple

import networkx as nx


class NodeType(Enum):
    """Enumeration of supported node types."""
    DOMAIN = "domain"
    IP = "ip"
    ASN = "asn"
    LARGE_ENTITY = "large_entity"
    CORRELATION_OBJECT = "correlation_object"

    def __repr__(self):
        return self.value


class GraphManager:
    """
    Thread-safe graph manager for DNSRecon infrastructure mapping.
    Uses NetworkX for in-memory graph storage with confidence scoring.
    Compatible with unified ProviderResult data model.
    """

    def __init__(self):
        """Initialize empty directed graph."""
        self.graph = nx.DiGraph()
        self.creation_time = datetime.now(timezone.utc).isoformat()
        self.last_modified = self.creation_time
        self.correlation_index = {}
        # Compile regex for date filtering for efficiency
        self.date_pattern = re.compile(r'^\d{4}-\d{2}-\d{2}[ T]\d{2}:\d{2}:\d{2}')
        self.EXCLUDED_KEYS = ['confidence', 'provider', 'timestamp', 'type','cert_validity_period_days','cert_source']

    def __getstate__(self):
        """Prepare GraphManager for pickling, excluding compiled regex."""
        state = self.__dict__.copy()
        # Compiled regex patterns are not always picklable
        if 'date_pattern' in state:
            del state['date_pattern']
        return state

    def __setstate__(self, state):
        """Restore GraphManager state and recompile regex."""
        self.__dict__.update(state)
        self.date_pattern = re.compile(r'^\d{4}-\d{2}-\d{2}[ T]\d{2}:\d{2}:\d{2}')

    def process_correlations_for_node(self, node_id: str):
        """
        UPDATED: Process correlations for a given node with enhanced tracking.
        Now properly tracks which attribute/provider created each correlation.
        """
        if not self.graph.has_node(node_id):
            return

        node_attributes = self.graph.nodes[node_id].get('attributes', [])
        
        # Process each attribute for potential correlations
        for attr in node_attributes:
            attr_name = attr.get('name')
            attr_value = attr.get('value')
            attr_provider = attr.get('provider', 'unknown')

            # Skip excluded attributes and invalid values
            if any(excluded_key in attr_name for excluded_key in self.EXCLUDED_KEYS) or not isinstance(attr_value, (str, int, float, bool)) or attr_value is None:
                continue

            if isinstance(attr_value, bool):
                continue
                
            if isinstance(attr_value, str) and (len(attr_value) < 4 or self.date_pattern.match(attr_value)):
                continue

            # Initialize correlation tracking for this value
            if attr_value not in self.correlation_index:
                self.correlation_index[attr_value] = {
                    'nodes': set(),
                    'sources': []  # Track which provider/attribute combinations contributed
                }

            # Add this node and source information
            self.correlation_index[attr_value]['nodes'].add(node_id)
            
            # Track the source of this correlation value
            source_info = {
                'node_id': node_id,
                'provider': attr_provider,
                'attribute': attr_name,
                'path': f"{attr_provider}_{attr_name}"
            }
            
            # Add source if not already present (avoid duplicates)
            existing_sources = [s for s in self.correlation_index[attr_value]['sources'] 
                              if s['node_id'] == node_id and s['path'] == source_info['path']]
            if not existing_sources:
                self.correlation_index[attr_value]['sources'].append(source_info)

            # Create correlation node if we have multiple nodes with this value
            if len(self.correlation_index[attr_value]['nodes']) > 1:
                self._create_enhanced_correlation_node_and_edges(attr_value, self.correlation_index[attr_value])

    def _create_enhanced_correlation_node_and_edges(self, value, correlation_data):
        """
        UPDATED: Create correlation node and edges with raw provider data (no formatting).
        """
        correlation_node_id = f"corr_{hash(str(value)) & 0x7FFFFFFF}"
        nodes = correlation_data['nodes']
        sources = correlation_data['sources']
        
        # Create or update correlation node
        if not self.graph.has_node(correlation_node_id):
            # Use raw provider/attribute data - no formatting
            provider_counts = {}
            for source in sources:
                # Keep original provider and attribute names
                key = f"{source['provider']}_{source['attribute']}"
                provider_counts[key] = provider_counts.get(key, 0) + 1
            
            # Use the most common provider/attribute as the primary label (raw)
            primary_source = max(provider_counts.items(), key=lambda x: x[1])[0] if provider_counts else "unknown_correlation"
            
            metadata = {
                'value': value,
                'correlated_nodes': list(nodes),
                'sources': sources,
                'primary_source': primary_source,
                'correlation_count': len(nodes)
            }
            
            self.add_node(correlation_node_id, NodeType.CORRELATION_OBJECT, metadata=metadata)
            print(f"Created correlation node {correlation_node_id} for value '{value}' with {len(nodes)} nodes")

        # Create edges from each node to the correlation node
        for source in sources:
            node_id = source['node_id']
            provider = source['provider']
            attribute = source['attribute']
            
            if self.graph.has_node(node_id) and not self.graph.has_edge(node_id, correlation_node_id):
                # Format relationship label as "corr_provider_attribute"
                relationship_label = f"corr_{provider}_{attribute}"
                
                self.add_edge(
                    source_id=node_id,
                    target_id=correlation_node_id,
                    relationship_type=relationship_label,
                    confidence_score=0.9,
                    source_provider=provider,
                    raw_data={
                        'correlation_value': value,
                        'original_attribute': attribute,
                        'correlation_type': 'attribute_matching'
                    }
                )
                
                print(f"Added correlation edge: {node_id} -> {correlation_node_id} ({relationship_label})")


    def _has_direct_edge_bidirectional(self, node_a: str, node_b: str) -> bool:
        """
        Check if there's a direct edge between two nodes in either direction.
        Returns True if node_a→node_b OR node_b→node_a exists.
        """
        return (self.graph.has_edge(node_a, node_b) or 
                self.graph.has_edge(node_b, node_a))

    def _correlation_value_matches_existing_node(self, correlation_value: str) -> bool:
        """
        Check if correlation value contains any existing node ID as substring.
        Returns True if match found (correlation node should NOT be created).
        """
        correlation_str = str(correlation_value).lower()
        
        # Check against all existing nodes
        for existing_node_id in self.graph.nodes():
            if existing_node_id.lower() in correlation_str:
                return True
        
        return False

    def _find_correlation_nodes_with_same_pattern(self, node_set: set) -> List[str]:
        """
        Find existing correlation nodes that have the exact same pattern of connected nodes.
        Returns list of correlation node IDs with matching patterns.
        """
        correlation_nodes = self.get_nodes_by_type(NodeType.CORRELATION_OBJECT)
        matching_nodes = []
        
        for corr_node_id in correlation_nodes:
            # Get all nodes connected to this correlation node
            connected_nodes = set()
            
            # Add all predecessors (nodes pointing TO the correlation node)
            connected_nodes.update(self.graph.predecessors(corr_node_id))
            
            # Add all successors (nodes pointed TO by the correlation node)  
            connected_nodes.update(self.graph.successors(corr_node_id))
            
            # Check if the pattern matches exactly
            if connected_nodes == node_set:
                matching_nodes.append(corr_node_id)
        
        return matching_nodes

    def _merge_correlation_values(self, target_node_id: str, new_value: Any, corr_data: Dict) -> None:
        """
        Merge a new correlation value into an existing correlation node.
        Uses same logic as large entity merging.
        """
        if not self.graph.has_node(target_node_id):
            return
            
        target_metadata = self.graph.nodes[target_node_id]['metadata']
        
        # Get existing values (ensure it's a list)
        existing_values = target_metadata.get('values', [])
        if not isinstance(existing_values, list):
            existing_values = [existing_values]
        
        # Add new value if not already present
        if new_value not in existing_values:
            existing_values.append(new_value)
            
        # Merge sources
        existing_sources = target_metadata.get('sources', [])
        new_sources = corr_data.get('sources', [])
        
        # Create set of unique sources based on (node_id, path) tuples
        source_set = set()
        for source in existing_sources + new_sources:
            source_tuple = (source['node_id'], source.get('path', ''))
            source_set.add(source_tuple)
        
        # Convert back to list of dictionaries
        merged_sources = [{'node_id': nid, 'path': path} for nid, path in source_set]
        
        # Update metadata
        target_metadata.update({
            'values': existing_values,
            'sources': merged_sources,
            'correlated_nodes': list(set(target_metadata.get('correlated_nodes', []) + corr_data.get('nodes', []))),
            'merge_count': len(existing_values),
            'last_merge_timestamp': datetime.now(timezone.utc).isoformat()
        })
        
        # Update description to reflect merged nature
        value_count = len(existing_values)
        node_count = len(target_metadata['correlated_nodes'])
        self.graph.nodes[target_node_id]['description'] = (
            f"Correlation container with {value_count} merged values "
            f"across {node_count} nodes"
        )
        
    def add_node(self, node_id: str, node_type: NodeType, attributes: Optional[List[Dict[str, Any]]] = None,
                description: str = "", metadata: Optional[Dict[str, Any]] = None) -> bool:
        """
        Add a node to the graph, update attributes, and process correlations.
        Now compatible with unified data model - attributes are dictionaries from converted StandardAttribute objects.
        """
        is_new_node = not self.graph.has_node(node_id)
        if is_new_node:
            self.graph.add_node(node_id, type=node_type.value,
                                added_timestamp=datetime.now(timezone.utc).isoformat(),
                                attributes=attributes or [],  # Store as a list from the start
                                description=description,
                                metadata=metadata or {})
        else:
            # Safely merge new attributes into the existing list of attributes
            if attributes:
                existing_attributes = self.graph.nodes[node_id].get('attributes', [])
                
                # Handle cases where old data might still be in dictionary format
                if not isinstance(existing_attributes, list):
                    existing_attributes = []

                # Create a set of existing attribute names for efficient duplicate checking
                existing_attr_names = {attr['name'] for attr in existing_attributes}

                for new_attr in attributes:
                    if new_attr['name'] not in existing_attr_names:
                        existing_attributes.append(new_attr)
                        existing_attr_names.add(new_attr['name'])
                
                self.graph.nodes[node_id]['attributes'] = existing_attributes
            if description:
                self.graph.nodes[node_id]['description'] = description
            if metadata:
                existing_metadata = self.graph.nodes[node_id].get('metadata', {})
                existing_metadata.update(metadata)
                self.graph.nodes[node_id]['metadata'] = existing_metadata

        self.last_modified = datetime.now(timezone.utc).isoformat()
        return is_new_node

    def add_edge(self, source_id: str, target_id: str, relationship_type: str,
                confidence_score: float = 0.5, source_provider: str = "unknown",
                raw_data: Optional[Dict[str, Any]] = None) -> bool:
        """
        UPDATED: Add or update an edge between two nodes with raw relationship labels.
        """
        if not self.graph.has_node(source_id) or not self.graph.has_node(target_id):
            return False

        new_confidence = confidence_score
        
        # UPDATED: Use raw relationship type - no formatting
        edge_label = relationship_type
        
        if self.graph.has_edge(source_id, target_id):
            # If edge exists, update confidence if the new score is higher.
            if new_confidence > self.graph.edges[source_id, target_id].get('confidence_score', 0):
                self.graph.edges[source_id, target_id]['confidence_score'] = new_confidence
                self.graph.edges[source_id, target_id]['updated_timestamp'] = datetime.now(timezone.utc).isoformat()
                self.graph.edges[source_id, target_id]['updated_by'] = source_provider
            return False

        # Add a new edge with raw attributes
        self.graph.add_edge(source_id, target_id,
                            relationship_type=edge_label,
                            confidence_score=new_confidence,
                            source_provider=source_provider,
                            discovery_timestamp=datetime.now(timezone.utc).isoformat(),
                            raw_data=raw_data or {})
        self.last_modified = datetime.now(timezone.utc).isoformat()
        return True

    def extract_node_from_large_entity(self, large_entity_id: str, node_id_to_extract: str) -> bool:
        """
        Removes a node from a large entity's internal lists and updates its count.
        This prepares the large entity for the node's promotion to a regular node.
        """
        if not self.graph.has_node(large_entity_id):
            return False
            
        node_data = self.graph.nodes[large_entity_id]
        attributes = node_data.get('attributes', {})
        
        # Remove from the list of member nodes
        if 'nodes' in attributes and node_id_to_extract in attributes['nodes']:
            attributes['nodes'].remove(node_id_to_extract)
            # Update the count
            attributes['count'] = len(attributes['nodes'])
        else:
            # This can happen if the node was already extracted, which is not an error.
            print(f"Warning: Node {node_id_to_extract} not found in the 'nodes' list of {large_entity_id}.")
            return True # Proceed as if successful
            
        self.last_modified = datetime.now(timezone.utc).isoformat()
        return True

    def remove_node(self, node_id: str) -> bool:
        """Remove a node and its connected edges from the graph."""
        if not self.graph.has_node(node_id):
            return False

        # Remove node from the graph (NetworkX handles removing connected edges)
        self.graph.remove_node(node_id)

        # Clean up the correlation index
        keys_to_delete = []
        for value, data in self.correlation_index.items():
            if isinstance(data, dict) and 'nodes' in data:
                # Updated correlation structure
                if node_id in data['nodes']:
                    data['nodes'].discard(node_id)
                    # Remove sources for this node
                    data['sources'] = [s for s in data['sources'] if s['node_id'] != node_id]
                if not data['nodes']:  # If no other nodes are associated, remove it
                    keys_to_delete.append(value)
            else:
                # Legacy correlation structure (fallback)
                if isinstance(data, set) and node_id in data:
                    data.discard(node_id)
                if not data:
                    keys_to_delete.append(value)
        
        for key in keys_to_delete:
            if key in self.correlation_index:
                del self.correlation_index[key]

        self.last_modified = datetime.now(timezone.utc).isoformat()
        return True

    def get_node_count(self) -> int:
        """Get total number of nodes in the graph."""
        return self.graph.number_of_nodes()

    def get_edge_count(self) -> int:
        """Get total number of edges in the graph."""
        return self.graph.number_of_edges()

    def get_nodes_by_type(self, node_type: NodeType) -> List[str]:
        """Get all nodes of a specific type."""
        return [n for n, d in self.graph.nodes(data=True) if d.get('type') == node_type.value]

    def get_neighbors(self, node_id: str) -> List[str]:
        """Get all unique neighbors (predecessors and successors) for a node."""
        if not self.graph.has_node(node_id):
            return []
        return list(set(self.graph.predecessors(node_id)) | set(self.graph.successors(node_id)))

    def get_high_confidence_edges(self, min_confidence: float = 0.8) -> List[Tuple[str, str, Dict]]:
        """Get edges with confidence score above a given threshold."""
        return [(u, v, d) for u, v, d in self.graph.edges(data=True)
                if d.get('confidence_score', 0) >= min_confidence]

    def get_graph_data(self) -> Dict[str, Any]:
        """
        Export graph data formatted for frontend visualization.
        SIMPLIFIED: No certificate styling - frontend handles all visual styling.
        """
        nodes = []
        for node_id, attrs in self.graph.nodes(data=True):
            node_data = {
                'id': node_id, 
                'label': node_id, 
                'type': attrs.get('type', 'unknown'),
                'attributes': attrs.get('attributes', []), # Raw attributes list
                'description': attrs.get('description', ''),
                'metadata': attrs.get('metadata', {}),
                'added_timestamp': attrs.get('added_timestamp')
            }
            
            # Add incoming and outgoing edges to node data
            if self.graph.has_node(node_id):
                node_data['incoming_edges'] = [
                    {'from': u, 'data': d} for u, _, d in self.graph.in_edges(node_id, data=True)
                ]
                node_data['outgoing_edges'] = [
                    {'to': v, 'data': d} for _, v, d in self.graph.out_edges(node_id, data=True)
                ]
            
            nodes.append(node_data)

        edges = []
        for source, target, attrs in self.graph.edges(data=True):
            edges.append({
                'from': source, 
                'to': target,
                'label': attrs.get('relationship_type', ''),
                'confidence_score': attrs.get('confidence_score', 0),
                'source_provider': attrs.get('source_provider', ''),
                'discovery_timestamp': attrs.get('discovery_timestamp')
            })
        
        return {
            'nodes': nodes, 
            'edges': edges,
            'statistics': self.get_statistics()['basic_metrics']
        }

    def export_json(self) -> Dict[str, Any]:
        """Export complete graph data as a JSON-serializable dictionary."""
        graph_data = nx.node_link_data(self.graph) # Use NetworkX's built-in robust serializer
        return {
            'export_metadata': {
                'export_timestamp': datetime.now(timezone.utc).isoformat(),
                'graph_creation_time': self.creation_time,
                'last_modified': self.last_modified,
                'total_nodes': self.get_node_count(),
                'total_edges': self.get_edge_count(),
                'graph_format': 'dnsrecon_v1_unified_model'
            },
            'graph': graph_data,
            'statistics': self.get_statistics()
        }

    def _get_confidence_distribution(self) -> Dict[str, int]:
        """Get distribution of edge confidence scores with empty graph handling."""
        distribution = {'high': 0, 'medium': 0, 'low': 0}
        
        # FIXED: Handle empty graph case
        if self.get_edge_count() == 0:
            return distribution
            
        for _, _, data in self.graph.edges(data=True):
            confidence = data.get('confidence_score', 0)
            if confidence >= 0.8:
                distribution['high'] += 1
            elif confidence >= 0.6:
                distribution['medium'] += 1
            else:
                distribution['low'] += 1
        return distribution

    def get_statistics(self) -> Dict[str, Any]:
        """Get comprehensive statistics about the graph with proper empty graph handling."""
        
        # FIXED: Handle empty graph case properly
        node_count = self.get_node_count()
        edge_count = self.get_edge_count()
        
        stats = {
            'basic_metrics': {
                'total_nodes': node_count,
                'total_edges': edge_count,
                'creation_time': self.creation_time,
                'last_modified': self.last_modified
            },
            'node_type_distribution': {}, 
            'relationship_type_distribution': {},
            'confidence_distribution': self._get_confidence_distribution(),
            'provider_distribution': {}
        }
        
        # FIXED: Only calculate distributions if we have data
        if node_count > 0:
            # Calculate node type distributions
            for node_type in NodeType:
                count = len(self.get_nodes_by_type(node_type))
                if count > 0:  # Only include types that exist
                    stats['node_type_distribution'][node_type.value] = count
        
        if edge_count > 0:
            # Calculate edge distributions
            for _, _, data in self.graph.edges(data=True):
                rel_type = data.get('relationship_type', 'unknown')
                stats['relationship_type_distribution'][rel_type] = stats['relationship_type_distribution'].get(rel_type, 0) + 1
                
                provider = data.get('source_provider', 'unknown')
                stats['provider_distribution'][provider] = stats['provider_distribution'].get(provider, 0) + 1
        
        return stats

    def clear(self) -> None:
        """Clear all nodes, edges, and indices from the graph."""
        self.graph.clear()
        self.correlation_index.clear()
        self.creation_time = datetime.now(timezone.utc).isoformat()
        self.last_modified = self.creation_time