Advanced Topic Modeling Techniques for Categorizing Software Vulnerabilities

2026-07-04

AI Summary

This study addresses the challenge of efficiently processing vast amounts of unstructured software vulnerability text, which hinders effective threat analysis and prioritization. To overcome this limitation, the authors propose a novel approach that integrates large language models (Llama2 and Mixtral) with advanced topic modeling techniques—namely BERTopic, Top2Vec, and CombinedTM—combined with UMAP or PCA for dimensionality reduction and HDBSCAN or DBSCAN for clustering. This pipeline automatically extracts interpretable semantic patterns from the "threat" fields of vulnerability reports and generates meaningful vulnerability categories. The method substantially enhances the interpretability, scalability, and automation of vulnerability classification, thereby providing robust support for timely and informed cybersecurity decision-making.

Abstract

The increasing complexity and frequency of software vulnerabilities demand efficient methods to analyze and prioritize threats. Traditional approaches often fail to process the vast amount of unstructured textual data effectively, highlighting the need for advanced solutions. This study leverages state-of-the-art topic modeling techniques powered by large language models (LLMs) to extract meaningful insights from the 'Threat' feature of a software vulnerability dataset. Models such as BERTopic, Top2Vec, CombinedTM, Llama2 with BERTopic, and Mixtral are utilized, along with dimensionality reduction and clustering methods like UMAP, PCA, HDBSCAN, and DBSCAN. By uncovering latent patterns and generating interpretable clusters, this research enhances threat prioritization and decision-making in cybersecurity. The findings support scalable and automated solutions for vulnerability management, contributing to improved security practices.

Problem

• software vulnerabilities
• topic modeling
• threat prioritization
• unstructured textual data
• cybersecurity

Innovation

• Topic Modeling
• Large Language Models
• Software Vulnerabilities
• Clustering
• Threat Prioritization