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

• Published in: Sensors (Basel, Switzerland) Vol. 24; no. 14; p. 4591
• Main Authors: Begum, Khadija, Mozumder, Md Ariful Islam, Joo, Moon-Il, Kim, Hee-Cheol
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 15.07.2024
• Subjects: Accuracy; Algorithms; Blockchain; Confidentiality; Datasets; federated learning; Internet of Medical Things (IoMT); Internet of Things; intrusion detection system; Machine learning; Neural networks; Privacy; security; Security systems
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s24144591

The Internet of Medical Things (IoMT) has significantly advanced healthcare, but it has also brought about critical security challenges. Traditional security solutions struggle to keep pace with the dynamic and interconnected nature of IoMT systems. Machine learning (ML)-based Intrusion Detection Systems (IDS) have been increasingly adopted to counter cyberattacks, but centralized ML approaches pose privacy risks due to the single points of failure (SPoFs). Federated Learning (FL) emerges as a promising solution, enabling model updates directly on end devices without sharing private data with a central server. This study introduces the BFLIDS, a Blockchain-empowered Federated Learning-based IDS designed to enhance security and intrusion detection in IoMT networks. Our approach leverages blockchain to secure transaction records, FL to maintain data privacy by training models locally, IPFS for decentralized storage, and MongoDB for efficient data management. Ethereum smart contracts (SCs) oversee and secure all interactions and transactions within the system. We modified the FedAvg algorithm with the Kullback–Leibler divergence estimation and adaptive weight calculation to boost model accuracy and robustness against adversarial attacks. For classification, we implemented an Adaptive Max Pooling-based Convolutional Neural Network (CNN) and a modified Bidirectional Long Short-Term Memory (BiLSTM) with attention and residual connections on Edge-IIoTSet and TON-IoT datasets. We achieved accuracies of 97.43% (for CNNs and Edge-IIoTSet), 96.02% (for BiLSTM and Edge-IIoTSet), 98.21% (for CNNs and TON-IoT), and 97.42% (for BiLSTM and TON-IoT) in FL scenarios, which are competitive with centralized methods. The proposed BFLIDS effectively detects intrusions, enhancing the security and privacy of IoMT networks.