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 scores.
—The proposed study suggests a to help cope with issues related to cybersecurity in Internet of Things and Industrial Internet of Things environments without compromising privacy. 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 intrusion detection systems. It also integrates supervised classification with autoencoder-based anomaly detection to detect existing and emerging cyberattacks. The proposed system was assessed with respect to the extended Industrial Internet of Things Intrusion Dataset (X-IIoT) and Network-Based Botnet Attack detection for IoT (N-BaIoT) benchmark datasets, where the environments were simulated as federated ones. The accuracy of the Hybrid Robust Federated Intrusion Detection System increased to 97.15% on X-IIoT and 97.64% on N-BaIoT with only 41 communication rounds and was resilient against up to 20% of Byzantine clients. These results showcase its efficacy to secure, private and communication-efficient intrusion detection for next generation Internet of Things and Hybrid Robust Federated Intrusion Detection System networks.
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 present paper suggests a Blockchain-Secured Federated Deep Reinforcement Learning (BS-FDRL) system to privacy-guaranteed and resilient IIoT control. The suggested system has distributed edge agents, which locally train DRL models and exchange only encrypted policy updates through federated learning; this guarantees the privacy of data. A smart contract-based blockchain layer allows aggregation and safe validation of model parameters to be tampered with. Also, differential privacy is integrated to safeguard sensitive industrial data in the process of policy exchange. The framework is coded with Python 3.10, DDPG/PPO algorithms with PyTorch, Flower to coordinate federated, and Hyperledger Fabric to integrate with blockchains. Industrial control and anomaly detection experimental analyses show that performance improvements are significant. The increase in the cumulative reward of the proposed model is 18-22% and the convergence rate is about 28 times higher than the baseline approaches. Stability of control is improved to 91.3% and communication overhead is cut down by up to 25%. Moreover, the overall energy consumption is reduced by approximately 65%, which enhances the efficiency of the system. Security analysis indicates a 96% poisoning attack detection and 94% inference attack resistance. The system is robust and only 35% accuracy deteriorates in adverse conditions. The results prove that BS-FDRL offers scalable, secure, and efficient communication-based intelligent control in Industry 4.0 IIoT setups.
