System Identification With Binary-Valued Observations Under Data Tampering Attacks

• 成果类型: Article
• 署名作者: Guo, Jin; Wang, Xuebin; Xue, Wenchao; Zhao, Yanlong
• 署名单位: University of Science & Technology Beijing; Chinese Academy of Sciences; Academy of Mathematics & System Sciences, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2020.3029325
• 发表日期: 2021
• 页码: 3825-3832
• 关键词: estimation; Sensor systems; security; communication networks; automation; Production; Binary-valued observations; compensation-oriented defense scheme; data tampering attack; System identification

摘要

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.