The Internet of Things (IoT) contains many devices that can compute and communicate, creating large networks. Industrial Internet of Things (IIoT) represents a developed application of IoT, connecting with embedded technologies in production in industrial operational settings to offer sophisticated automation and real-time decisions. Still, IIoT compels significant cybersecurity threats beyond jamming and spoofing, which could ruin the critical infrastructure. Developing a robust Intrusion Detection System (IDS) addresses the challenges and vulnerabilities present in these systems. Traditional IDS methods have achieved high detection accuracy but need improved scalability and privacy issues from large datasets. This paper proposes a Fog-enabled Federated Learning-based Intrusion Detection System (FFL-IDS) utilizing Convolutional Neural Network (CNN) that mitigates these limitations. This framework allows multiple parties in IIoT networks to train deep learning models with data privacy preserved and low-latency detection ensured using fog computing. The proposed FFL-IDS is validated on two datasets, namely the Edge-IIoTset, explicitly tailored to environments with IIoT, and CIC-IDS2017, comprising various network scenarios. On the Edge-IIoTset dataset, it achieved 93.4% accuracy, 91.6% recall, 88% precision, 87% F1 score, and 87% specificity for jamming and spoofing attacks. The system showed better robustness on the CIC-IDS2017 dataset, achieving 95.8% accuracy, 94.9% precision, 94% recall, 93% F1 score, and 93% specificity. These results establish the proposed framework as a scalable, privacy-preserving, high-performance solution for securing IIoT networks against sophisticated cyber threats across diverse environments.

This study gives an in-depth evaluation of deep learning algorithms for threat classifying in the Internet of Medical Things, or IoMT, devices, a rapidly expanding subset of the Internet of Things (IoT) essential for modern healthcare systems. IoMT devices are more susceptible to cyberattacks as they gather, send, and process sensitive medical data, endangering patient security and privacy. Due to particular limitations like low processing power and strong privacy regulations, traditional security solutions usually need to catch up in IoMT circumstances. Consequently, deep learning algorithms offer viable substitutes for real-time, adaptive attack classification due to their capacity to identify intricate patterns in big datasets. Our evaluation and classification of state-of-the-art deep learning algorithms for IoMT security focuses on classification accuracy, computational efficiency, and threat adaption. We discuss significant challenges and opportunities, compare strategies, and assess success metrics. We examine CNNs, RNNs, and hybrid architectures, highlighting how well they can withstand various assaults. This survey will be a thorough resource to direct future studies and advancements in deep learning-based IoMT security.