FFL-IDS: A Fog-Enabled Federated Learning-Based Intrusion Detection System to Counter Jamming and Spoofing Attacks for the Industrial Internet of Things

Abstract

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.