Shaesta Khan, Sheh Rahman and Noraziah, Adzhar and Nazri, Ahmad Zamani (2024) Comparative analysis of machine learning models to predict common vulnerabilities and exposure. Malaysian Journal of Fundamental and Applied Sciences, 20 (6). pp. 1410-1419. ISSN 2289-599x. (Published)
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Abstract
Predicting Common Vulnerabilities and Exposures (CVE) is a challenging task due to the increasing complexity of cyberattacks and the vast amount of threat data available. Effective prediction models are crucial for enabling cybersecurity teams to respond quickly and prevent potential exploits. This study aims to provide a comparative analysis of machine learning techniques for CVE prediction to enhance proactive vulnerability management and strengthening cybersecurity practices. The supervised machine learning model which is Gaussian Naive Bayes and unsupervised machine learning models that utilize clustering algorithms which are K-means and DBSCAN were employed for the predictive modelling. The performance of these models was compared using performance metrics such as accuracy, precision, recall, and F1-score. Among these models, the Gaussian Naive Bayes achieved an accuracy rate of 99.79%, and outperformed the clustering-based machine learning models in effectively determining the class labels or results of the data it was trained on or tested against. The outcome of this study will provide a proof of concept to Cybersecurity Malaysia, offering insights into the CVE model.
| Item Type: | Article |
|---|---|
| Additional Information: | Indexed by Scopus |
| Uncontrolled Keywords: | Accuracy; Common vulnerabilities and exposures; Cyber threat; Unsupervised and supervised machine learning models |
| Subjects: | Q Science > QA Mathematics Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Faculty/Division: | Center for Mathematical Science Institute of Postgraduate Studies |
| Depositing User: | Mrs. Nurul Hamira Abd Razak |
| Date Deposited: | 14 Mar 2025 05:08 |
| Last Modified: | 14 Mar 2025 05:08 |
| URI: | http://umpir.ump.edu.my/id/eprint/44067 |
| Download Statistic: | View Download Statistics |
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