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πŸ“„ IEEE Conference Paper
Cybersecurity AI Research

Knowledge Graph-Enhanced RAG for Cyber Threat Intelligence

Advanced hybrid AI system integrating Neo4j knowledge graphs with RAG-based LLM for intelligent APT analysis using MITRE ATT&CK framework

Hit Rate
87.2%
Mean Reciprocal Rank
0.84
Response Time
1.5 seconds
Graph Nodes
1,427
View System Architecture View Performance Results

Advancing Cybersecurity through Intelligent Knowledge Integration

"This research bridges the gap between structured knowledge representation and generative AI, enabling explainable cyber threat analysis and significantly advancing automated cybersecurity intelligence capabilities."

Cybersecurity Intelligence Challenge

Modern cybersecurity faces unprecedented challenges from Advanced Persistent Threats (APTs) that employ sophisticated zero-day exploits, ransomware campaigns, and nation-state cyber warfare tactics. Traditional security measures struggle to process heterogeneous threat intelligence in real-time, creating critical gaps in threat detection and response capabilities.

The absence of machine-readable knowledge bases for APT analysis severely limits automated reasoning and contextual understanding of multi-stage attacks, while static text-based approaches fail to capture the dynamic, interconnected nature of evolving cyber threats.

Critical Intelligence Gaps:

  • Fragmented Threat Data: Heterogeneous intelligence scattered across multiple sources without unified structure
  • Limited Contextual Understanding: Lack of semantic relationships between threat actors, tactics, and techniques
  • Reactive Defense Posture: Insufficient proactive threat mitigation due to poor automated reasoning
  • Scalability Constraints: Manual analysis processes unable to handle volume and velocity of threat intelligence
  • Hallucination in AI Systems: Generative AI models producing unreliable threat assessments without grounding

Hybrid AI Solution Architecture

Our research introduces a knowledge graph-enhanced RAG framework that revolutionizes cyber threat intelligence by integrating structured knowledge representation with advanced generative AI capabilities, creating an intelligent system for real-time APT analysis and attribution.

MITRE ATT&CK Framework Integration

The system is trained on comprehensive MITRE ATT&CK data, ensuring detailed coverage of APT groups, tactics, and techniques with up-to-date threat intelligence for accurate analysis and attribution.

Core Innovation Components:

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Neo4j Knowledge Graph
1,427 nodes and 2,543 relationships systematically organizing APT groups, tactics, techniques, and software dependencies
🎯
Vector Embeddings
Sentence-BERT (All-Mpnet-V2) generating 768-dimensional dense vectors for semantic similarity search
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Pinecone Vector Database
High-performance vector search with cosine similarity metrics for rapid threat intelligence retrieval
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Fine-tuned Llama 3.1
RAG-enhanced language model delivering context-aware, grounded responses for cybersecurity professionals

Technical Architecture:

Neo4j Python Sentence-BERT Pinecone Llama 3.1 MITRE ATT&CK spaCy NLP TF-IDF

Research Collaboration:

Ansh Srivastava
Lead Researcher
RVCE
Aditya Saiprasad
AI Systems Developer
RVCE
Karthik Prakash
ML Engineer
RVCE
Bandaru Jnyanadeep
Cybersecurity Specialist
RVCE
Advaith A
Knowledge Graph Engineer
RVCE
Dr. Rajesh R
Research Supervisor
DRDO CAIR

System Architecture & Processing Pipeline

The system implements a dual-pipeline architecture combining offline knowledge graph construction with online RAG-based query processing for optimal performance and accuracy in threat intelligence retrieval.

Knowledge Graph to RAG Integration Pipeline

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MITRE Data Ingestion
APT Groups, Tactics, Techniques
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Neo4j Graph Construction
Structured Relationships
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Vector Embeddings
Sentence-BERT Encoding
🎯
Pinecone Indexing
Similarity Search
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RAG Response
Llama 3.1 Generation

Advanced Processing Components:

  • Knowledge Graph Creation: Systematic organization of 1,427 nodes representing APT groups, tactics, techniques, and software
  • Multi-representation Embeddings: Main text, descriptions, relationships, and word-level vectors for comprehensive semantic coverage
  • NLP-Enhanced Query Processing: spaCy-based tokenization, POS tagging, and cybersecurity-specific term extraction
  • Weighted Embedding Fusion: 70% full-query + 30% token-level matches with domain-specific term prioritization
  • Graph-Connected Re-ranking: PageRank centrality and connectivity scoring for authoritative result prioritization

Mathematical Framework:

Core Similarity Computation:

Cosine Similarity: cos_sim(vq, vnode) = (vq Β· vnode) / (||vq|| ||vnode||)
Weighted Embedding: E(Q) = Ξ£(wi Β· E(ti)) where wi = POS + NER + domain weights
Score Fusion: S = 0.7 Β· S_full + 0.3 Β· S_token

Performance Results & Empirical Evaluation

Comprehensive evaluation demonstrates exceptional performance across multiple metrics, validating the effectiveness of our hybrid knowledge graph-RAG approach for real-time cyber threat intelligence analysis.

Core Performance Metrics:

87.2%
Hit Rate
APT technique identification accuracy
0.84
Mean Reciprocal Rank
Ranking quality assessment
90%
NER Precision
Named entity recognition accuracy
1.5s
Average Query Latency
Real-time response capability
91.4%
Relevancy Score
Contextual response accuracy
97.5%
Success Rate
System reliability metric

System Scalability Metrics:

1,427
Knowledge Graph Nodes
APT entities and relationships
2,543
Graph Relationships
Interconnected threat patterns
768
Embedding Dimensions
Vector representation depth
92%
Context Relevance
Graph-based re-ranking effectiveness

Qualitative Analysis Insights:

  • Explainable Intelligence: Graph-grounded responses provide clear attribution pathways for threat analysis
  • Reduced Hallucination: Knowledge graph grounding significantly improves response accuracy over pure LLM approaches
  • Real-time Capability: Sub-2-second response times enable interactive threat hunting and analysis
  • Scalable Architecture: Modular design supports expansion to additional threat intelligence sources
  • Domain Expertise: Cybersecurity-specific NLP processing outperforms general-purpose retrieval systems

Comparative Advantages:

Research Contributions:

  • Novel Hybrid Architecture: First integration of Neo4j knowledge graphs with RAG for cybersecurity
  • Multi-modal Retrieval: Combines vector similarity with graph connectivity for superior accuracy
  • Domain-specific Optimization: Cybersecurity-tailored NLP processing and query expansion
  • Empirical Validation: Comprehensive evaluation with real-world threat intelligence datasets

Research Methodology & Innovation

This research represents a significant advancement in knowledge-driven cybersecurity AI, introducing novel methodologies that bridge structured knowledge representation with generative AI capabilities.

Methodological Innovations:

  • Dual-Pipeline Architecture: Offline knowledge graph construction with online RAG processing for optimal performance
  • Multi-representation Embeddings: Comprehensive semantic coverage through main text, descriptions, and relationships
  • Weighted Query Fusion: Domain-specific term prioritization with POS tagging and NER enhancement
  • Graph-enhanced Re-ranking: PageRank centrality and connectivity scoring for authoritative result prioritization

Technical Contributions:

  • MITRE ATT&CK Integration: Systematic knowledge graph construction from standardized threat intelligence
  • Sentence-BERT Optimization: All-Mpnet-V2 model fine-tuning for cybersecurity domain specificity
  • Pinecone Vector Search: High-performance similarity search with cosine distance optimization
  • Llama 3.1 RAG Enhancement: Context-aware generation with knowledge graph grounding

Evaluation Framework:

  • Hit Rate Analysis: Ground truth validation for APT technique identification accuracy
  • Ranking Quality Assessment: Mean Reciprocal Rank (MRR) evaluation for result prioritization
  • Named Entity Recognition: Precision measurement for cybersecurity-specific term extraction
  • Latency Profiling: Real-time performance analysis across query complexity spectrum

Future Research Directions

This foundational work opens multiple avenues for advanced cybersecurity AI research, with potential for significant impact on threat intelligence automation and defense system capabilities.

Technical Enhancements:

  • Real-time Knowledge Graph Updates: Dynamic ingestion of emerging threat intelligence and IOCs
  • Multi-modal Intelligence Integration: Incorporation of network logs, malware samples, and behavioral data
  • Advanced Graph Neural Networks: Deep learning approaches for enhanced relationship modeling
  • Federated Learning Integration: Privacy-preserving threat intelligence sharing across organizations

Operational Applications:

  • Proactive Threat Hunting: AI-driven hypothesis generation for security operations centers
  • Automated Incident Response: Context-aware playbook generation for threat remediation
  • Attribution Analytics: Enhanced APT group identification and campaign tracking
  • Predictive Intelligence: Early warning systems for emerging attack patterns

Research Impact:

  • Academic Contribution: Novel framework for knowledge-enhanced retrieval in cybersecurity
  • Industry Application: Practical deployment in SOC environments and threat intelligence platforms
  • Defense Innovation: Advanced capabilities for national cybersecurity operations
  • Open Source Community: Reproducible research enabling broader security research advancement
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