Deception Attacks on Remote Estimation With Disclosure and Disruption Resources

• 成果类型: Article
• 署名作者: Li, Yuzhe; Yang, Yake; Zhao, Zhengen; Zhou, Jing; Quevedo, Daniel E.
• 署名单位: Northeastern University - China; Nanjing University of Aeronautics & Astronautics; University of Alberta; Queensland University of Technology (QUT)
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2022.3202981
• 发表日期: 2023
• 页码: 4096-4112
• 关键词: Cyber-physical systems; deception attacks; remote state estimation; sequential Kalman filters

摘要

The problem of deception attacks utilizing both disclosure and disruption resources in the multiple types of sensors setting against remote state estimation is investigated in this article. A group of sensors, including reliable and unreliable ones, measure system states and transmit innovations to a remote fusion center via wireless channels, wherein a malicious attacker can modify the data packets of unreliable sensors. By virtue of disclosure resources of reliable sensors and disruption resources of unreliable ones, a stealthy linear attack strategy is given, which can deceive the anomaly detectors and degrade the estimation performance. In this article, we obtain feasible criteria under which such attack policies can bypass existing detectors. The evolution of estimation error covariance at the remote end under the attacks is analyzed. Then, a closed-form expression of the optimal linear deception attack maximizing estimation error covariance is derived. Moreover, extended results on the designed attack strategies and attackers with extra measurements are provided. Finally, two illustrative examples with stable and unstable processes are given to verify the effectiveness of proposed methods.