A data‐based private learning framework for enhanced security against replay attacks in cyber‐physical systems. (22nd June 2020)

• Record Type: Journal Article
• Authors: Zhai, Lijing; Vamvoudakis, Kyriakos G.
• Other Names: Wan Yan guestEditor.; Yang Tao guestEditor.; Yuan Ye guestEditor.; Lewis Frank L. guestEditor.
• Is Part Of: International journal of robust and nonlinear control. Volume 31:Number 6(2021)
• Page Start: 1817
• Page End: 1833
• Publication Date: 2020-06-22
• Subjects: ADP -- CPS -- watermarking signals -- zero‐sum LQG games; Automatic control -- Periodicals; Control theory -- Periodicals; Nonlinear systems -- Periodicals; 629.836
• DOI: 10.1002/rnc.5040
• Languages: English
• ISSNs: 1049-8923

Abstract

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.