MARC details
| 000 -LEADER |
|---|
| fixed length control field |
03818nrm a2200349 i 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
|---|
| control field |
MY-KuUP |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
|---|
| control field |
20260406170755.0 |
| 006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS |
|---|
| 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 |
|---|
| fixed length control field |
260406t20252025my a|||fr|||| 000 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
THE0010435 (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) |
FKOM .A36 2025 r Thesis |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Che Akmal Bin Che Yahaya, |
| Relator term |
author. |
| 245 10 - TITLE STATEMENT |
|---|
| Title |
Mobile malware detection using word cloud with deep learning and root exploit analysis / |
| Statement of responsibility, etc. |
Che Akmal Bin Che Yahaya |
| 246 ## - VARYING FORM OF TITLE |
|---|
| Title proper/short title |
|
| 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 |
2025 |
| 264 #6 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Date of production, publication, distribution, manufacture, or copyright notice |
© 2025 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
iv, 200 pages : |
| Other physical details |
illustration (some colour) ; |
| Dimensions |
30 cm. + |
| 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 |
Faculty of Computing |
| 502 ## - DISSERTATION NOTE |
|---|
| Dissertation note |
Thesis (Doctor of Philosophy) -- Universiti Malaysia Pahang Al Sultan Abdullah - 2025 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc. note |
Include bibliographical reference |
| 520 3# - SUMMARY, ETC. |
|---|
| Summary, etc. |
Mobile malware continues to pose a critical threat by executing malicious activities such as stealing sensitive information, conducting unauthorized data collection, and running covert processes on infected devices. Ordinary malware families, including root exploits, botnets, trojans, and ransomware, constantly evolve as attackers refine their techniques to evade detection. Existing detection strategies rely on dynamic and static analysis. Dynamic analysis, which observes malware during execution, can detect complex behaviors but demands extensive computational resources and monitoring time. Static analysis, which examines code structure without execution, is more resource-efficient but faces limitations in selecting optimal features, especially when malicious traits emerge only through specific co-occurrence patterns of tokens. This feature selection challenge is exacerbated by the large and constantly changing set of potential attributes, including permissions, system calls, and intents. Moreover, automated feature selection picks clues one at a time; consequently, it overlooks crucial interactions and discards informative cross-feature patterns, yielding models that appear well-performing in tests yet fail to generalize to novel or obfuscated applications. To address these limitations, this study introduces a novel approach that leverages word cloud visualization as an intermediate feature representation for static malware detection. By converting complete source code into word cloud images, the method captures both frequency and co-occurrence cues of critical code tokens in a single visual space, enabling a deeper semantic representation without the need for manually defining feature categories or quantities. These word cloud images are then processed by a Convolutional Neural Network (CNN), allowing automatic feature extraction and pattern recognition, thus overcoming the traditional bottleneck of manual feature engineering. The proposed model was evaluated using a balanced dataset comprising 25,000 malware samples from the Android Malware Dataset (AMD) and 25,000 benign samples. Experimental results demonstrate that the method achieved an accuracy of 93.02% in predicting previously unseen malware variants, highlighting its ability to generalize detection to novel threats. The integration of word cloud visualization with CNN not only enhances detection accuracy but also provides a scalable, category-independent framework for combating the evolving landscape of mobile malware. Word cloud, Android apps, Static analysis, Mobile malware Detection system, Deep learning |
| 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 |
| General subdivision |
Dissertations |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Library of Congress Classification |
| Koha item type |
Thesis |