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Research Article · Open access · Published 23 January 2024

Securing Mobile Devices from Malware: A Faceoff Between Federated Learning and Deep Learning Models for Android Malware Classification

Narayan Subramanian1, Logesh Ravi2, Mithin Jain Shaan1, Malathi Devarajan1, Tanupriya Choudhury3,6, Ketan Kotecha4 & Subramaniyaswamy Vairavasundaram5

Journal of Computer Science, Volume 20, Issue 3, pp. 254–264 · doi.org/10.3844/jcssp.2024.254.264

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Abstract

Amidst the escalating threats of android malware, urgency mounts to detect issues while safeguarding user privacy. Traditional machine learning and deep learning methods, dealt with scalability challenges and privacy compromises, finding a potential remedy in federated learning. This study introduces a groundbreaking federated learning-based methodology and compares federated learning with traditional deep learning techniques for Android malware classification, employing renowned datasets, including Drebin, Malgenome, Tuandromd, and Kronodroid. Shifting gears, a federated learning-based approach for malware classification excels in accuracy, scalability, and privacy preservation. Acknowledging limitations and ethical considerations, the study underscores the need for robust privacy measures and dataset transparency. This study unveils federated learning's prowess in android malware classification, opening doors to privacy-driven applications in diverse domains.

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Open access. This article is licensed under a Creative Commons Attribution 4.0 International Licence (CC BY 4.0), as printed on the article, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original authors and the source. No permission is needed.

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Subramanian, N., Ravi, L., Shaan, M. J., Devarajan, M., Choudhury, T., Kotecha, K. & Vairavasundaram, S. (2024). Securing Mobile Devices from Malware: A Faceoff Between Federated Learning and Deep Learning Models for Android Malware Classification. Journal of Computer Science, 20(3), 254–264. https://doi.org/10.3844/jcssp.2024.254.264

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Volume 20, Issue 3
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Securing Mobile Devices from Malware: A Faceoff Between Federated Learning and Deep Learning Models for Android Malware ClassificationJournal of Computer Science · Volume 20, Issue 3 · pp. 254–264 · CC BY 4.0Download