False Data Injection Attacks Against State Estimation Without Knowledge of Estimators

• 成果类型: Article
• 署名作者: Lu, An-Yang; Yang, Guang-Hong
• 署名单位: Northeastern University - China; Northeastern University - China
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2022.3161259
• 发表日期: 2022
• 页码: 4529-4540
• 关键词: security; State estimation; detectors; Linear systems; estimation error; Eigenvalues and eigenfunctions; actuators; cyber-physical systems (CPSs); false data injection attacks; nonperfect attack design; perfect and state estimation

摘要

This article investigates the stealthy false data injection attack design problem for a class of cyber physical systems equipped with state estimators and attack detectors. The objective is to worsen the estimation performance without triggering any alarm. First, a necessary and sufficient condition for the existence of perfect attacks, which alter the state estimate without affecting residual signals, is provided. It is shown that the estimation error can be arbitrarily large under the well-designed perfect attacks. Second, if the perfect attacks do not exist, the existence of nonperfect attacks, which worsen the estimation performance with a degree influence on residual signals, is analyzed. It is shown that the desired nonperfect attack sequence can be designed by analyzing the maximum eigenvalue and the corresponding eigenvector of an auxiliary matrix. Compared with the existing methods, in this article, attacks are designed without knowledge of estimators and can be injected from any time point. Finally, a numerical simulation is provided to illustrate the effectiveness of the proposed methods.