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Federated learning-guided intrusion detection and neural key exchange for safeguarding patient data on the internet of medical things

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Abstract

To improve the security of the Internet of Medical Things (IoMT) in healthcare, this paper offers a Federated Learning (FL)-guided Intrusion Detection System (IDS) and an Artificial Neural Network (ANN)-based key exchange mechanism inside a blockchain framework. The IDS are essential for spotting network anomalies and taking preventative action to guarantee the secure and dependable functioning of IoMT systems. The suggested method integrates FL-IDS with a blockchain-based ANN-based key exchange mechanism, providing several important benefits: (1) FL-based IDS creates a shared ledger that aggregates nearby weights and transmits historical weights that have been averaged, lowering computing effort, eliminating poisoning attacks, and improving data visibility and integrity throughout the shared database. (2) The system uses edge-based detection techniques to protect the cloud in the case of a security breach, enabling quicker threat recognition with less computational and processing resource usage. FL’s effectiveness with fewer data samples plays a part in this benefit. (3) The bidirectional alignment of ANNs ensures a strong security framework and facilitates the production of keys inside the IoMT network on the blockchain. (4) Mutual learning approaches synchronize ANNs, making it easier for IoMT devices to distribute synchronized keys. (5) XGBoost and ANN models were put to the test using BoT-IoT datasets to gauge how successful the suggested method is. The findings show that ANN demonstrates greater performance and dependability when dealing with heterogeneous data available in IoMT, such as ICU (Intensive Care Unit) data in the medical profession, compared to alternative approaches studied in this study. Overall, this method demonstrates increased security measures and performance, making it an appealing option for protecting IoMT systems, especially in demanding medical settings like ICUs.

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Authors and Affiliations

  1. School of Public Health and Management, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China

    Chongzhou Zhong

  2. Department of Computer Science and Electronics, Ramakrishna Mission Vidyamandira, Belur Math, Howrah, 711202, West Bengal, India

    Arindam Sarkar & Sarbajit Manna

  3. Department of Computer Science and Information, Taibah University, 42353, Medina, Saudi Arabia

    Mohammad Zubair Khan

  4. Department of Electrical Engineering, Umm Al-Qura University, Mecca, Saudi Arabia

    Abdulfattah Noorwali

  5. Department of Computer Science and Engineering, Supreme Knowledge Foundation Group of Institutions, Chandannagar, West Bengal, India

    Ashish Das & Koyel Chakraborty

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Zhong, C., Sarkar, A., Manna, S. et al. Federated learning-guided intrusion detection and neural key exchange for safeguarding patient data on the internet of medical things. Int. J. Mach. Learn. & Cyber. 15, 5635–5665 (2024). https://doi.org/10.1007/s13042-024-02269-2

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