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ISSN: 1549-3636 (Print)
ISSN: 1552-6607 (Online)
Research Article Open Access

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 and Subramaniyaswamy Vairavasundaram5
  • 1 School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, India
  • 2 School of Electronics Engineering, Centre for Advanced Data Science, Vellore Institute of Technology, Chennai, Tamil Nadu, India
  • 3 Department of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun, 248002, Uttarakhand, India
  • 4 Symbiosis Centre for Applied Artificial Intelligence, Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, India
  • 5 School of Computing, SASTRA Deemed University, Thanjavur, Tamilnadu, India
  • 6 Department of Computer Science and Engineering, Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, Maharashtra, India

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.

Journal of Computer Science
Volume 20 No. 3, 2024, 254-264

DOI: https://doi.org/10.3844/jcssp.2024.254.264

Submitted On: 20 October 2023 Published On: 23 January 2024

How to Cite: 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

Open AccessCopyright: © 2024 Narayan Subramanian, Logesh Ravi, Mithin Jain Shaan, Malathi Devarajan, Tanupriya Choudhury, Ketan Kotecha and Subramaniyaswamy Vairavasundaram. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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Keywords

  • Android Malware
  • Machine Learning
  • Deep Learning
  • Federated Learning
  • Privacy Preservation
  • Scalability