Network Vulnerability Assessment based on Knowledge Graph

Vulnerability assessment is a critical aspect of cybersecurity, and its importance has grown significantly. However, traditional methods based on attack graph are expensive and lack interpretability. And emerging methods based on knowledge graph, there is currently no widely accepted scheme in the industry. To address this issue, we propose a new scheme named KG-Rank, which leverages the correlations between vulnerabilities and assets through a knowledge graph and improves PageRank to assess and rank vulnerabilities. The KG-Rank scheme involves constructing Vulnerability Knowledge Graph (VKG) and Weakness Knowledge Graph (WKG), and obtaining new relations between weaknesses through relational reasoning on WKG by using a relational reasoning model called Doc2TransR to complete VKG. We then transform the nodes and edges in VKG and assess nodes on the transformed graph using a designed random walk strategy. Our experimental results demonstrate the effectiveness and reliability of KG-Rank, which considers not only the severity of vulnerabilities but also the importance of assets and the exposure scope of vulnerabilities and assets.