Comparative analysis of machine learning models to predict common vulnerabilities and exposure - Shaesta Khan, Sheh Rahman; Noraziah, Adzhar; Nazri, Ahmad Zamani (Penerbit UTM Press, 2024)
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.