Association Analysis and Prediction of Network Security Vulnerabilities Based on Knowledge Graph

Abstract

This research presents a novel approach for network security vulnerability association analysis and prediction leveraging knowledge graph technology. We construct a comprehensive vulnerability knowledge graph that captures semantic relationships between vulnerabilities, attack patterns, and affected systems by integrating data from multiple sources including NVD, CVE, and vendor security bulletins. Our methodology encompasses three complementary analysis approaches: semantic association analysis using path-based algorithms, temporal association analysis employing multi-scale time-series techniques, and attack chain association analysis through exploitation chain construction. The prediction framework combines knowledge graph embeddings, graph neural networks, and multi-modal feature fusion to forecast vulnerability exploitation with 89.2% accuracy within a 30-day window, significantly outperforming statistical baselines (71.3%) and non-knowledge graph methods (82.6%). Experimental evaluation on real-world datasets demonstrates that our semantic association analysis achieved 0.87 precision and 0.82 recall (F1: 0.84), outperforming baselines by 18.7%. Our attack chain discovery identified 76.8% of known attack chains while discovering 23 previously undocumented but plausible vectors. The system maintained 83.7% performance with 30% missing attributes, demonstrating robust adaptability to real-world challenges. In enterprise deployment, our approach identified 37 critical vulnerability associations and predicted 14 high-priority vulnerabilities, with 11 being missed by existing tools. The methodology aids in the proactive cybersecurity management in networks that are becoming increasingly complex.