Volume 22, Issue 4 (3-2026)
JSDP 2026, 22(4): 70-53 |
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Olyaei Torqabeh A, Rasoolzadegan A. A Deep Learning Based Method for Android Malware Detection. JSDP 2026; 22 (4) : 4
URL: http://jsdp.rcisp.ac.ir/article-1-1455-en.html
URL: http://jsdp.rcisp.ac.ir/article-1-1455-en.html
Associate Professor of Software Engineering, Department of Computer Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
Abstract: (89 Views)
The Android operating system, an open-source platform supported by Google, has become a cornerstone of modern technology due to its widespread adoption in diverse devices, including smartphones, smart TVs, and wearables. This extensive reach has established Android as a dominant force in the global market but simultaneously made it a primary target for malware developers. The growing sophistication and frequency of mobile malware attacks pose significant challenges for users and Android app distribution platforms. These attacks exploit the open nature of the Android ecosystem and increasingly employ advanced techniques such as obfuscation, rendering traditional detection methods less effective. In response to these challenges, this study introduces an innovative approach to malware detection leveraging image and audio processing in combination with deep learning techniques. Our proposed methodology addresses the limitations of existing methods by providing a scalable, high-accuracy solution suitable for industrial deployment. The research is based on static analysis. During the static analysis, executable file bytes are transformed into audio signals, and features extracted from these signals are used to train a deep learning model. This model achieved an impressive accuracy of 99.3%, with a precision of 99.8% and a recall of 99.1%. The novelty of our approach lies in its ability to detect obfuscated malware, a critical and challenging aspect of modern malware detection. By mapping executable files to the audio domain in static analysis, our method effectively reduces computational complexity while enhancing detection accuracy. The proposed framework was validated on a diverse and comprehensive dataset, showcasing its capability to distinguish between benign and malicious applications with high reliability. Furthermore, the method's design ensures practical applicability in real-world scenarios, particularly in app distribution platforms where rapid and accurate malware detection is crucial. This research contributes a novel, efficient, and scalable malware detection solution that addresses the challenges posed by obfuscation and computational demands. The proposed framework not only advances the state-of-the-art in Android malware detection but also lays the groundwork for future research exploring hybrid analysis techniques and real-time detection capabilities.
Article number: 4
Keywords: Android Malware Detection, Deep Learning, Static Analysis, Image Processing, Audio Signal Processing
Type of Study: Research |
Subject:
Paper
Received: 2025/02/1 | Accepted: 2026/02/3 | Published: 2026/03/20 | ePublished: 2026/03/20
Received: 2025/02/1 | Accepted: 2026/02/3 | Published: 2026/03/20 | ePublished: 2026/03/20
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