Enhanced Cyber Threat Severity Prediction Leveraging CVSS and BERT-Based Language Models

Abstract

: Accurate cyber threat severity prediction is essential for proactive risk management and efficient resource allocation. Traditional approaches mainly rely on the Common Vulnerability Scoring System (CVSS), which inadequately handles threats exploiting multiple vulnerabilities simultaneously. In this paper, we propose a methodology for cyber threat severity prediction. First, we introduce two aggregation strategies, sequential and parallel exploitation, to compute overall threat severity scores from CVSS metrics while considering multiple exploited vulnerabilities. Second, we propose a predictive model based on Large Language Model (LLM) encoders, namely BERT and the cybersecurity-oriented SecureBERT+. The model processes vulnerability descriptions to generate contextual embeddings, applies attention mechanisms to highlight informative features, and uses Long Short-Term Memory (LSTM) layers to capture contextual dependencies before predicting the threat severity score. Experimental results show improved predictive accuracy and lower error rates, particularly with SecureBERT+.