MARC details
| 000 -LEADER |
|---|
| fixed length control field |
02283ntm a2200337 i 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
|---|
| control field |
MY-KuUP |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
|---|
| control field |
20251125111038.0 |
| 006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS |
|---|
| fixed length control field |
t||||fs|||| 000 0 |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION |
|---|
| fixed length control field |
ta |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
250506t20232023my a|||fs|||| 000 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
THE0010036 (Local) |
| Qualifying information |
Hardback |
| 040 ## - CATALOGING SOURCE |
|---|
| Original cataloging agency |
UMPSA |
| Language of cataloging |
eng |
| Transcribing agency |
UMPSA |
| Description conventions |
rda |
| 090 ## - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (RLIN) |
|---|
| Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) |
PSM .K43 2023 r Bc. |
| 100 0# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Shaesta Khan Sheh Rahman, |
| Relator term |
author. |
| 245 10 - TITLE STATEMENT |
|---|
| Title |
Exploration of machine learning approach to predictive models on common vulnerabilities and exposure (CVE) dataset / |
| Statement of responsibility, etc. |
Shaesta Khan Binti Sheh Rahman |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Place of production, publication, distribution, manufacture |
Kuantan Pahang : |
| Name of producer, publisher, distributor, manufacturer |
UMPSA, |
| Date of production, publication, distribution, manufacture, or copyright notice |
2023 |
| 264 #4 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Date of production, publication, distribution, manufacture, or copyright notice |
© 2023 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xiv, 84 pages : |
| Other physical details |
Illustrations (some colour) ; |
| Accompanying material |
1 CD-ROM. |
| 336 ## - CONTENT TYPE |
|---|
| Source |
rdacontent |
| Content type term |
text |
| 337 ## - MEDIA TYPE |
|---|
| Source |
rdamedia |
| Media type term |
unmediated |
| 338 ## - CARRIER TYPE |
|---|
| Source |
rdacarrier |
| Carrier type term |
volume |
| 347 ## - DIGITAL FILE CHARACTERISTICS |
|---|
| Source |
rda |
| File type |
text file |
| Encoding format |
PDF |
| 500 ## - GENERAL NOTE |
|---|
| General note |
Center for Mathematical Sciences |
| 502 ## - DISSERTATION NOTE |
|---|
| Dissertation note |
Bachelor of Applied Science In Data Analytics With Honour -- Universiti Malaysia Pahang – 2023 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc. note |
Includes bibliographical references |
| 520 3# - SUMMARY, ETC. |
|---|
| Summary, etc. |
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. |
| 610 20 - SUBJECT ADDED ENTRY--CORPORATE NAME |
|---|
| Corporate name or jurisdiction name as entry element |
Center for Mathematical Sciences |
| General subdivision |
Dissertations |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Universities and colleges |
| General subdivision |
Dissertations |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Final Year Report |
| General subdivision |
Dissertations |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Library of Congress Classification |
| Koha item type |
Final Year Report |