AN EXPLORATORY EXAMINATION OF SOFTWARE VULNERABILITY CLASSIFICATION USING LARGE LANGUAGE MODELS

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dc.contributor.advisorPeruma, Anthony
dc.contributor.authorOliveira Araujo, Ana Catarina
dc.contributor.departmentComputer Science
dc.date.accessioned2024-07-02T23:41:16Z
dc.date.issued2024
dc.description.degreeM.S.
dc.embargo.liftdate2025-06-24
dc.identifier.urihttps://hdl.handle.net/10125/108322
dc.subjectComputer science
dc.subjectCVE
dc.subjectcybersecurity
dc.subjectLLMs
dc.subjectsoftware vulnerability
dc.subjectVDO
dc.subjectvulnerability classification
dc.titleAN EXPLORATORY EXAMINATION OF SOFTWARE VULNERABILITY CLASSIFICATION USING LARGE LANGUAGE MODELS
dc.typeThesis
dcterms.abstractSoftware vulnerabilities are critical weaknesses that can compromise the security of a system. While current research primarily focuses on automating the classification and detection of them using a range of machine learning models, there remains a notable gap in integrating ontologies like the Vulnerability Description Ontology with Large Language Models (LLMs) for enhanced classification accuracy. Our study utilizes the National Vulnerability Database (NVD) and the National Institute of Standards and Technology’s Vulnerability Description Ontology framework to enhance the clas- sification of these vulnerabilities. The methodology involves an in-depth analysis of NVD data and an investigation of the effectiveness of various LLMs to analyze vulnerability descriptions across 27 vulnerability categories in 5 noun groups. Our findings reveal that LLMs, particularly BERT and DistilBERT, demonstrate stronger performance when compared to traditional machine learn- ing models and entropy-based methods. Moreover, while expanding the dataset aims to capture a broader range of vulnerabilities, its effectiveness varies, highlighting the crucial role of annotation quality. This research emphasizes the importance of advanced machine learning techniques and quality data annotation in optimizing vulnerability assessment processes in cybersecurity.
dcterms.extent64 pages
dcterms.languageen
dcterms.publisherUniversity of Hawai'i at Manoa
dcterms.rightsAll UHM dissertations and theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission from the copyright owner.
dcterms.typeText
local.identifier.alturihttp://dissertations.umi.com/hawii:12160

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M.S. - Computer Science