02283ntm a2200337 i 4500 0 0 lcc 0 6 10000 10000 2025-05-06 0 CD13595 T000003193 2025-05-06 00:00:00 2025-05-06 PSM 102502 102508 MY-KuUP 20251125111038.0 t||||fs|||| 000 0 ta 250506t20232023my a|||fs|||| 000 0 eng d THE0010036 (Local) Hardback UMPSA eng UMPSA rda PSM .K43 2023 r Bc. Shaesta Khan Sheh Rahman, author. Exploration of machine learning approach to predictive models on common vulnerabilities and exposure (CVE) dataset / Shaesta Khan Binti Sheh Rahman Kuantan Pahang : UMPSA, 2023 © 2023 xiv, 84 pages : Illustrations (some colour) ; 1 CD-ROM. rdacontent text rdamedia unmediated rdacarrier volume rda text file PDF Center for Mathematical Sciences Bachelor of Applied Science In Data Analytics With Honour -- Universiti Malaysia Pahang – 2023 Includes bibliographical references Threat intelligence involves gathering and analysing information on cyber threats to improve organizations' understanding of attackers and enable a faster response to issues. Hackers are becoming more sophisticated and can exploit vulnerabilities, leading to various security risks. This study aims to predict Common Vulnerability and Exposure by analysing different models and methods, enhancing proactive vulnerability management, and strengthening cybersecurity practices. The dataset used in this study is obtained from Kaggle. Unsupervised machine learning methods is utilised which is clustering algorithms. Clustering algorithms like K-means, Gaussian Naive Bayes, and DBSCAN are utilized for predictive modelling. In effectively determining the class labels or results of the data it was trained on or tested against, the Gaussian Naive Bayes model achieved an accuracy rate of 99.79% among these models. The goal is to provide a proof of concept to Cybersecurity Malaysia, offering insights into the CVE model. Center for Mathematical Sciences Dissertations Universities and colleges Dissertations Final Year Report Dissertations lcc PSM