Deep Federated Learning-Based Cyber-Attack Detection in Industrial Control Systems

Due to the differences between Information Technology (IT) and Industrial Control System (ICS) networks, current IT security solutions are not working effectively on ICS networks. Moreover, due to security and privacy issues, ICS owners usually do not share their network data with third parties to train specific machine learning-based ICS security solutions. To rectify the mentioned issues, a scalable deep federated learning-based method is presented in this paper. In the proposed method, each client trains an unsupervised deep neural network model using local data and shares its parameters with a server. The server aggregates the clients’ parameters, makes a generalized public model, and shares it with all clients. The proposed model is evaluated using a real-world ICS dataset in a water treatment system and compared with two non-federated learning-based methods. Findings show that the proposed method outperformed the other two methods with the same computational complexity as other deep neural network-based methods in the literature.