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 o...

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Bibliographic Details
Published inInternational journal of robust and nonlinear control Vol. 31; no. 6; pp. 1817 - 1833
Main Authors Zhai, Lijing, Vamvoudakis, Kyriakos G.
Format Journal Article
LanguageEnglish
Published Bognor Regis Wiley Subscription Services, Inc 01.04.2021
Subjects
ADP
CPS
Learning
Privacy
Watermarking
watermarking signals
zero‐sum LQG games
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Summary: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.
Bibliography:Funding information
Department of Energy, No. DE‐EE0008453; NSF, Nos. CAREER CPS‐1851588; S&AS 1849198; ONR Minerva, No. N00014‐18‐1‐2160
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1049-8923
1099-1239
DOI:10.1002/rnc.5040