TL;DR
The proposed hybrid approach outperforms ML-only and blockchain-only baselines, offering a scalable, secure, and real-time IDS for IIoT infrastructures.
The study proposes a secure and adaptive intrusion detection model using Federated Learning and Blockchain, augmented with autoencoder-based feature reduction, showing that combining FL, blockchain, and deep feature extraction offers a viable and secure solution for intrusion detection systems in IoT.
This research contributes a novel, energy-efficient, and scalable architecture for industrial data protection, setting a foundation for future integration with 6G-enabled IIoT systems, federated trust networks, and lightweight transformer-based threat detection frameworks.
BELS-IoT is proposed, a novel decentralized protection architecture that integrates a cryptocurrency-based blockchain layer with a multi-layer ensemble learning engine that rewards honest behavior and penalizes malicious activities while maintaining privacy through federated learning with blockchain-verified reputation...
The proposed framework introduces several innovative features, such as federated learning with momentum-based optimization, adaptive differential privacy, trust verification via blockchain, and Byzantine-resilient aggregation, to enhance the security, scalability, and robustness of the system compared with traditional...
The proposed framework effectively integrates encryption, federated intrusion detection, explainable artificial intelligence, and blockchain security to enhance privacy, transparency, and reliability in IoMT healthcare networks.
Industrial Internet of Things (IIoT) systems require dynamical, safe, and competent control to address the dynamic industrial processes. Centralized Deep Reinforcement Learning (DRL) methods are however vulnerable to privacy, high communication overheads, and model tampering. In order to address these shortcomings, the...
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