Data-Driven Attack Detection and Identification for Cyber-Physical Systems Under Sparse Sensor Attacks

• 成果类型: Article
• 署名作者: Zhao, Zhengen; Xu, Yunsong; Li, Yuzhe; Zhen, Ziyang; Yang, Ying; Shi, Yang
• 署名单位: Nanjing University of Aeronautics & Astronautics; National University of Defense Technology - China; Northeastern University - China; Peking University; University of Victoria
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2022.3230360
• 发表日期: 2023
• 页码: 6330-6337
• 关键词: Attack detection and identification; Cyber-physical systems; sparse attacks; sparse recovery; subspace method

摘要

This article studies the issues of data-driven attack detection and identification for cyber-physical systems under sparse sensor attacks. First, based on the available input and output datasets, a data-driven monitor is formulated with the following two objectives: attack detection and attack identification. Then, with the subspace approach, a data-driven attack detection policy is presented, wherein the attack detector is designed directly by the process data. A subspace projection-based attack identification scheme is proposed via designing a bank of projection filters to determine the locations of attacked sensors. Moreover, the sparse recovery technique is adopted to decrease the combinatorial complexity of the subspace projection-based identification method. The attack identification is recast into a block-sparse recovery problem. Finally, the proposed methods are verified by the simulations on a flight vehicle system.