Predicting Known Vulnerabilities from Attack Descriptions Using Sentence Transformers
Modern infrastructures rely on software systems that remain vulnerable to cyberattacks. These attacks frequently exploit vulnerabilities documented in repositories such as MITRE's Common Vulnerabilities and Exposures (CVE). However, Cyber Threat Intelligence resources, including MITRE ATT&CK and CVE, provide only partial coverage of attack-vulnerability relationships. Attack information often appears before vulnerabilities are formally linked, creating the need for automated methods that infer likely vulnerabilities directly from attack descriptions. This thesis addresses the problem of predicting known vulnerabilities from natural-language descriptions of cyberattacks. We develop transformer-based sentence embedding methods that encode attack and vulnerability descriptions into semantic vector representations, enabling similarity-based ranking and recommendation. Fourteen state-of-the-art transformer models were evaluated across four attack description types (Tactic, Technique, Procedure, and Attack Pattern). Results show that Technique descriptions in MITRE ATT&CK provide the strongest predictive signal. The multi-qa-mpnet-base-dot-v1 (MMPNet) model achieved the best performance due to its hybrid pre-training and optimization for semantic similarity. The approach was implemented in the VULDAT tool, which automatically links attacks to vulnerabilities. Manual validation revealed previously undocumented relationships in MITRE repositories. Evaluation on unseen cyberattack reports demonstrates that the models generalize beyond curated datasets and support proactive vulnerability awareness.