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<p>The growing adoption of Android-based Internet of Medical Things (IoMT) devices has unfortunately attracted the attention of cyber attackers who exploit vulnerabilities through malware infiltration, resulting in serious threats such as data theft and unauthorized access. However, directly sharing users’ raw data for centralized model training raises privacy concerns. To address these challenges, this paper proposes a federated deep learning-based framework for malware detection and data protection in edge-enabled intelligent healthcare systems. This framework utilizes a distributed deep neural network that is trained without exchanging user row data, ensuring high detection accuracy while preserving user privacy. Two real-world datasets are employed to evaluate the efficiency of the proposed approach, and a comparative analysis is conducted against a baseline approach across three test scenarios. Experimental results demonstrate the superiority of the proposed framework over the baseline techniques, achieving an impressive accuracy of 98.84% for malware identification while preserving users’ privacy.</p>

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Federated deep learning for malware detection and data protection in edge-enabled IoMT

  • 期刊论文

  • 发表日期：

    2025年9月15日

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  • Rahul Yadav,
  • U. Kumaran,
  • V. P. Meena,
  • Li Wei,
  • Guangsheng Feng,
  • Mohamed Abd Elaziz

摘要

The growing adoption of Android-based Internet of Medical Things (IoMT) devices has unfortunately attracted the attention of cyber attackers who exploit vulnerabilities through malware infiltration, resulting in serious threats such as data theft and unauthorized access. However, directly sharing users’ raw data for centralized model training raises privacy concerns. To address these challenges, this paper proposes a federated deep learning-based framework for malware detection and data protection in edge-enabled intelligent healthcare systems. This framework utilizes a distributed deep neural network that is trained without exchanging user row data, ensuring high detection accuracy while preserving user privacy. Two real-world datasets are employed to evaluate the efficiency of the proposed approach, and a comparative analysis is conducted against a baseline approach across three test scenarios. Experimental results demonstrate the superiority of the proposed framework over the baseline techniques, achieving an impressive accuracy of 98.84% for malware identification while preserving users’ privacy.