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Exploration of machine learning approach to predictive models on common vulnerabilities and exposure (CVE) dataset / Shaesta Khan Binti Sheh Rahman

By:

Shaesta Khan Sheh Rahman [author.]

Material type: Text

text

Media type:

unmediated

Carrier type:

volume

ISBN:

THE0010036 (Local)

Subject(s):

Dissertation note: Bachelor of Applied Science In Data Analytics With Honour -- Universiti Malaysia Pahang – 2023 Abstract: 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.

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Holdings ( 1 )
Title notes ( 4 )

Holdings
Item type	Current library	Call number	Status	Date due	Barcode
Final Year Report	UMPLIB GAMBANG	CD13595 (Browse shelf(Opens below))	In Transit		T000003193

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.