Federated Temporal Learning Based Cyber Attack Detection for Distributed Industrial IoT Systems

Industrial Internet of Things (IIoT) is widely applied in modern industries. However, IIoT faces challenges in system modeling and cyber security. Distributed data isolation influences the time series data modeling, while frequent communications over IIoT leave vulnerabilities to cyber attacks. To ensure proper IIoT operations, we propose a federated temporal learning (FTL) mechanism to extract temporal features for cyber attack detection. We build local FTL models to accurately extract local time series features on edge devices. We collaboratively construct the global FTL model to solve the distributed heterogeneity at edges to further improve cyber attack detection. Moreover, we build a testing platform and emulate realistic cyber attacks towards IIoT. The proposed FTL detects seven types of cyber attacks more effectively compared with traditional models, in terms of comprehensive evaluation metrics. Thus, FTL is a promising IIoT security solution for distributed industrial systems.