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

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.