Comparative analysis of machine learning models to predict common vulnerabilities and exposure
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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Penerbit UTM Press
2024
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| Online Access: | http://umpir.ump.edu.my/id/eprint/44067/ http://umpir.ump.edu.my/id/eprint/44067/1/Comparative%20analysis%20of%20machine%20learning%20models.pdf |
| _version_ | 1848827024055992320 |
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| author | Shaesta Khan, Sheh Rahman Noraziah, Adzhar Nazri, Ahmad Zamani |
| author_facet | Shaesta Khan, Sheh Rahman Noraziah, Adzhar Nazri, Ahmad Zamani |
| author_sort | Shaesta Khan, Sheh Rahman |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-15T03:54:08Z |
| format | Article |
| id | ump-44067 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:54:08Z |
| publishDate | 2024 |
| publisher | Penerbit UTM Press |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-440672025-03-14T05:08:42Z http://umpir.ump.edu.my/id/eprint/44067/ Comparative analysis of machine learning models to predict common vulnerabilities and exposure Shaesta Khan, Sheh Rahman Noraziah, Adzhar Nazri, Ahmad Zamani QA Mathematics QA75 Electronic computers. Computer science 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. Penerbit UTM Press 2024-12-16 Article PeerReviewed pdf en cc_by_nc_4 http://umpir.ump.edu.my/id/eprint/44067/1/Comparative%20analysis%20of%20machine%20learning%20models.pdf 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) https://doi.org/10.11113/mjfas.v20n6.3822 https://doi.org/10.11113/mjfas.v20n6.3822 |
| spellingShingle | QA Mathematics QA75 Electronic computers. Computer science Shaesta Khan, Sheh Rahman Noraziah, Adzhar Nazri, Ahmad Zamani Comparative analysis of machine learning models to predict common vulnerabilities and exposure |
| title | Comparative analysis of machine learning models to predict common vulnerabilities and exposure |
| title_full | Comparative analysis of machine learning models to predict common vulnerabilities and exposure |
| title_fullStr | Comparative analysis of machine learning models to predict common vulnerabilities and exposure |
| title_full_unstemmed | Comparative analysis of machine learning models to predict common vulnerabilities and exposure |
| title_short | Comparative analysis of machine learning models to predict common vulnerabilities and exposure |
| title_sort | comparative analysis of machine learning models to predict common vulnerabilities and exposure |
| topic | QA Mathematics QA75 Electronic computers. Computer science |
| url | http://umpir.ump.edu.my/id/eprint/44067/ http://umpir.ump.edu.my/id/eprint/44067/ http://umpir.ump.edu.my/id/eprint/44067/ http://umpir.ump.edu.my/id/eprint/44067/1/Comparative%20analysis%20of%20machine%20learning%20models.pdf |