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System Identification With Binary-Valued Observations Under Data Tampering Attacks

Jin Guo, Xuebin Wang, Wenchao Xue, Yanlong Zhao

2020IEEE Transactions on Automatic Control80 citationsDOI

Abstract

With the popularization and application of cyber–physical systems in many industry and infrastructure fields, the security issue has been quite an important concern. This article addresses the defense problem against the data tampering attack under the framework of system identification with binary-valued observations. From the perspective of the attacker, it is shown that how to achieve the maximum hit effect with the least attack energy. From the perspective of the defender, a so-called compensation-oriented defense approach is proposed, and the corresponding identification algorithm is designed. The strong consistency of the algorithm is proved, and the asymptotic normality is obtained, based on which the optimal defense scheme is established. A simulation example is provided to illustrate the effectiveness of the defense algorithm and the main theoretical results.

Topics & Concepts

Identification (biology)Perspective (graphical)Binary numberComputer scienceConsistency (knowledge bases)Identification schemeComputer securityCompensation (psychology)Scheme (mathematics)Energy (signal processing)NormalityAlgorithmData miningMathematicsArtificial intelligenceStatisticsArithmeticMathematical analysisPsychoanalysisPsychologyBotanyMeasure (data warehouse)BiologySmart Grid Security and ResilienceAdversarial Robustness in Machine LearningPhysical Unclonable Functions (PUFs) and Hardware Security