MARC details
| 000 -LEADER |
|---|
| fixed length control field |
03510ntm a2200373 i 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
MY-KuUP |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20251125110727.0 |
| 006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS |
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| fixed length control field |
t||||fr|||| 000 0 |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION |
|---|
| fixed length control field |
ta |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
230410t20222022my a|||fr|||| 000 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
THE0009598 (Local) |
| Qualifying information |
Hardback |
| 040 ## - CATALOGING SOURCE |
|---|
| Original cataloging agency |
UMP |
| Language of cataloging |
eng |
| Transcribing agency |
UMP |
| Description conventions |
rda |
| 090 ## - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (RLIN) |
|---|
| Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) |
FKOM .R38 2022 r Thesis |
| 100 0# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Sharfah Ratibah Tuan Mat, |
| Relator term |
author. |
| 245 10 - TITLE STATEMENT |
|---|
| Title |
Android mobile malware detection model based on permission features using machine learning approach / |
| Statement of responsibility, etc. |
Sharfah Ratibah Binti Tuan Mat |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Place of production, publication, distribution, manufacture |
Kuantan, Pahang : |
| Name of producer, publisher, distributor, manufacturer |
UMP, |
| Date of production, publication, distribution, manufacture, or copyright notice |
2022 |
| 264 #4 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Date of production, publication, distribution, manufacture, or copyright notice |
© 2022 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xv, 130 pages : |
| Other physical details |
illustrations (some color) ; |
| Dimensions |
30 cm. + |
| Accompanying material |
1 CD-ROM |
| 336 ## - CONTENT TYPE |
|---|
| Source |
rdacontent |
| Content type term |
text |
| 336 ## - CONTENT TYPE |
|---|
| Source |
rdacontent |
| Content type term |
text |
| 337 ## - MEDIA TYPE |
|---|
| Source |
rdamedia |
| Media type term |
unmediated |
| 337 ## - MEDIA TYPE |
|---|
| Source |
rdamedia |
| Media type term |
computer |
| 338 ## - CARRIER TYPE |
|---|
| Source |
rdacarrier |
| Carrier type term |
volume |
| 338 ## - CARRIER TYPE |
|---|
| Source |
rdacarrier |
| Carrier type term |
computer disc |
| 347 ## - DIGITAL FILE CHARACTERISTICS |
|---|
| Source |
rda |
| File type |
text file |
| Encoding format |
PDF |
| 500 ## - GENERAL NOTE |
|---|
| General note |
Faculty of Computing |
| 502 ## - DISSERTATION NOTE |
|---|
| Dissertation note |
Thesis (Master of Science) -- Universiti Malaysia Pahang – 2022 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc. note |
Includes bibliographical references |
| 520 3# - SUMMARY, ETC. |
|---|
| Summary, etc. |
The use of Android mobile devices has increased exponentially and gained massive popularity in the mobile market. It has become the most valuable item to humans across the world. The popularity and primary operating system of the Android mobile device have raised concerns over malware threats. Unscrupulous authors have deployed malicious software such as root exploit, botnet, Trojan horse, and spyware and published it on Google Play to gain profits. Android malware has the ability to abduct user credentials and cause a resource to maltreat. Different techniques have been adopted to detect and prevent the spread of Android malware, including anomaly, signature-based, and hybrid detection techniques. Nevertheless, current technologies indicate that Android malware attackers have find novel ways to avoid detection. This study aims to propose an Android malware detection model using Bayesian classifier and Multilayer perceptron classifier via static analysis technique to address the Android malware issue. This study focused on the permission feature of Android mobile devices. This study obtained two types of datasets which were retrieved from Androzoo and Drebin database. The first dataset contains 10,000 samples, and the second dataset contains 96,074 samples. Several experiments were conducted to learn the permission features’ behaviour and find the best accuracy for the approaches used. Chi-square and information gain algorithms were used for features selection. The aim is to learn the behaviour of permission features that react to the accuracy according to the number of features. Both samples of datasets then were evaluated using machine learning and deep learning approaches to analyse the best accuracy of malware detection. The validation of machine learning obtained 85.4% accuracy for 96,074 samples and 91.1% accuracy for 10,000 samples. The validation in deep learning obtained 98.02% accuracy for the 96,074 samples and 98% accuracy for the 10,000 samples. These best achievements for both datasets were from the deep learning approach. In conclusion, the accuracy of deep learning is always greater in smaller or larger datasets, and machine learning produces great detection in smaller datasets. |
| 610 20 - SUBJECT ADDED ENTRY--CORPORATE NAME |
|---|
| Corporate name or jurisdiction name as entry element |
Faculty of Computing |
| 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 |
Theses |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Library of Congress Classification |
| Koha item type |
Thesis |