Automation of Categorizing Software Vulnerabilities Using NLP Techniques

This research presents a comprehensive investigation into software vulnerabilities, employing a multifaceted approach encompassing exploratory data analysis, statistical examination, and advanced text mining techniques. The primary goal is to gain a nuanced understanding of diverse vulnerabilities and features within the dataset. Central to the study are Natural Language Processing (NLP) topic modeling techniques-Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA)-revealing latent themes within textual data. A meticulous manual analysis contrasts LSA and LDA outputs, evaluating coherence and contextual relevance in the vulnerability context. Objectives include comprehensive vulnerability insight and automated categorization of vulnerabilities. This research offers valuable insights into vulnerability management and NLP-based automation in cybersecurity contexts.