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BFLIDS: Blockchain-Driven Federated Learning for Intrusion Detection in IoMT Networks.

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Summary
This summary is machine-generated.

This study introduces BFLIDS, a novel system using blockchain and federated learning for secure intrusion detection in medical IoT networks. It enhances cybersecurity and data privacy without centralizing sensitive information.

Keywords:
Internet of Medical Things (IoMT)blockchainfederated learningintrusion detection systemprivacysecurity

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Area of Science:

  • Cybersecurity
  • Healthcare Technology
  • Machine Learning

Background:

  • The Internet of Medical Things (IoMT) presents significant security vulnerabilities.
  • Traditional security measures are inadequate for dynamic IoMT environments.
  • Centralized machine learning intrusion detection systems (IDS) raise privacy concerns due to single points of failure.

Purpose of the Study:

  • To develop a secure and privacy-preserving IDS for IoMT networks.
  • To enhance intrusion detection capabilities using a novel framework.
  • To address the limitations of centralized machine learning in IoMT security.

Main Methods:

  • Introduction of Blockchain-empowered Federated Learning-based IDS (BFLIDS).
  • Integration of blockchain for transaction security, federated learning for data privacy, IPFS for decentralized storage, and MongoDB for data management.
  • Modification of the FedAvg algorithm with Kullback-Leibler divergence and adaptive weighting.
  • Implementation of Adaptive Max Pooling-based CNN and modified BiLSTM with attention for classification.

Main Results:

  • Achieved high accuracies: 97.43% (CNNs/Edge-IIoTSet), 96.02% (BiLSTM/Edge-IIoTSet), 98.21% (CNNs/TON-IoT), and 97.42% (BiLSTM/TON-IoT) in federated learning scenarios.
  • Demonstrated competitive performance compared to centralized methods.
  • Validated the effectiveness of BFLIDS in detecting intrusions in IoMT networks.

Conclusions:

  • BFLIDS effectively enhances security and privacy in IoMT networks.
  • The proposed system offers a robust solution for intrusion detection in resource-constrained and sensitive environments.
  • Blockchain and federated learning integration provides a scalable and secure approach for IoMT cybersecurity.