Detecting cyberattacks using anomaly detection in industrial control systems: A Federated Learning approach

In recent years, the rapid development and wide application of advanced technologies have profoundly impacted industrial manufacturing, leading to smart manufacturing (SM). However, the Industrial IoT (IIoT)-based manufacturing systems are now one of the top industries targeted by a variety of attacks. In this research, we propose detecting Cyberattacks in Industrial Control Systems using Anomaly Detection. An anomaly detection architecture for the IIoT-based SM is proposed to deploy one of the top most concerned networking technique - a Federated Learning architecture - that can detect anomalies for time series data typically running inside an industrial system. The architecture achieves higher detection performance compared to the current detection solution for time series data. It also shows the feasibility and efficiency to be deployed on top of edge computing hardware of an IIoT-based SM that can save 35% of bandwidth consumed in the transmission link between the edge and the cloud. At the expense, the architecture needs to trade off with the computing resource consumed at edge devices for implementing the detection task. However, findings in maximal CPU usage of 85% and average Memory usage of 37% make this architecture totally realizable in an IIoT-based SM.