A data‐based private learning framework for enhanced security against replay attacks in cyber‐physical systems

Summary This article develops a data‐based and private learning framework of the detection and mitigation against replay attacks for cyber‐physical systems. Optimal watermarking signals are added to assist in the detection of potential replay attacks. In order to improve the confidentiality of the output data, we first add a level of differential privacy. We then use a data‐based technique to learn the best defending strategy in the presence of worst case disturbances, stochastic noise, and replay attacks. A data‐based Neyman‐Pearson detector design is also proposed to identify replay attacks. Finally, simulation results show the efficacy of the proposed approach along with a comparison of our data‐based technique to a model‐based one.