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Dew-Cloud-Based Hierarchical Federated Learning for Intrusion Detection in IoMT.

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    The study introduces a Dew-Cloud model for hierarchical federated learning (HFL) to enhance Internet of Medical Things (IoMT) security. This approach improves data privacy and protects sensitive health information from cyber threats.

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    Area of Science:

    • Cybersecurity
    • Health Informatics
    • Machine Learning

    Background:

    • The COVID-19 pandemic increased reliance on remote healthcare and Internet of Medical Things (IoMT) devices.
    • The expansion of IoMT has led to increased cyber threats targeting sensitive patient data.
    • Resource-constrained IoMT devices pose challenges for traditional security measures.

    Purpose of the Study:

    • To design a secure and privacy-preserving model for IoMT environments.
    • To address the growing cybersecurity risks in remote healthcare systems.
    • To improve the detection and prevention of malicious attacks on IoMT networks.

    Main Methods:

    • Development of a Dew-Cloud based model for hierarchical federated learning (HFL).
    • Deployment of a hierarchical long-term memory (HLSTM) model on distributed Dew servers with cloud computing backend.
    • Implementation of data pre-processing techniques to optimize model training.

    Main Results:

    • The proposed HFL-HLSTM model achieved high training accuracy (99.31%) and minimal training loss (0.034).
    • The model demonstrated superior performance in accuracy, precision, recall, and f-score compared to existing methods.
    • The Dew-Cloud model enhances data privacy and availability for critical IoMT applications.

    Conclusions:

    • The proposed HFL-HLSTM model offers a robust solution for securing IoMT devices and patient data.
    • This approach effectively mitigates cybersecurity risks in remote healthcare settings.
    • The model's performance indicates its potential for safeguarding the integrity of the healthcare system.