000			03818nrm a2200349 i 4500
003			MY-KuUP
005			20260406170755.0
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008			260406t20252025my a|||fr|||| 000 0 eng d
020			_aTHE0010435 (Local) _qHardback
040			_aUMPSA _beng _cUMPSA _erda
090			_aFKOM .A36 2025 r Thesis
100	1		_aChe Akmal Bin Che Yahaya, _eauthor.
245	1	0	_aMobile malware detection using word cloud with deep learning and root exploit analysis / _cChe Akmal Bin Che Yahaya
246			_a
264		1	_aKuantan, Pahang : _bUMPSA, _c2025
264		6	_c© 2025
300			_aiv, 200 pages : _billustration (some colour) ; _c30 cm. + _e1 CD-ROM
336			_2rdacontent _atext
337			_2rdamedia _aunmediated
338			_2rdacarrier _avolume
347			_2rda _atext file _bPDF
500			_aFaculty of Computing
502			_aThesis (Doctor of Philosophy) -- Universiti Malaysia Pahang Al Sultan Abdullah - 2025
504			_aInclude bibliographical reference
520	3		_aMobile 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	2	0	_aFaculty of Computing _xDissertations
650		0	_aUniversities and colleges _xDissertations
650		0	_aTheses _xDissertations
942			_2lcc _cTHESIS
999			_c104634 _d104640