Constructing a Knowledge Graph from Textual Descriptions of Software Vulnerabilities in the National Vulnerability Database Article Swipe

PDF

Related Concepts

Computer science Knowledge graph Vulnerability (computing) Vulnerability assessment Graph Heuristic Knowledge extraction Software Relation (database) Artificial intelligence Data mining Information retrieval Data science Computer security Theoretical computer science Programming language Psychology Psychological resilience Psychotherapist

Anders Mølmen Høst , Pierre Lison , Leon Moonen ·

YOU? · · 2023 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2305.00382 · OA: W4367694249

Knowledge graphs have shown promise for several cybersecurity tasks, such as vulnerability assessment and threat analysis. In this work, we present a new method for constructing a vulnerability knowledge graph from information in the National Vulnerability Database (NVD). Our approach combines named entity recognition (NER), relation extraction (RE), and entity prediction using a combination of neural models, heuristic rules, and knowledge graph embeddings. We demonstrate how our method helps to fix missing entities in knowledge graphs used for cybersecurity and evaluate the performance.