TY  - MANSCPT
AU  - Shaesta Khan Sheh Rahman,
TI  - Exploration of machine learning approach to predictive models on common vulnerabilities and exposure (CVE) dataset
SN  - THE0010036 (Local)
PY  - 2023///
CY  - Kuantan Pahang
PB  - UMPSA
KW  - Center for Mathematical Sciences
KW  - Dissertations
KW  - Universities and colleges
KW  - Final Year Report
N1  - Center for Mathematical Sciences; Bachelor of Applied Science In Data Analytics With Honour -- Universiti Malaysia Pahang – 2023; Includes bibliographical references
N2  - 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
ER  -