An Optimal Attack Strategy Based on Adversary-Modified Historical Innovations

This paper primarily explores the challenge of stealthy innovation-based attacks within cyber–physical systems. An optimal covert false data injection attack strategy is proposed, which involves modifying historical and current system innovations by the adversary. The approach differs from the existing ones in that it combines the historical and current innovation results. Utilizing less prior knowledge to obtain the innovations made by the adversary in the previous time step and integrating them with current innovations, a more generalized attack strategy is devised, leading to further performance enhancement in estimation. Through the design of an optimal attack strategy, the adversary aims to ensure stealthiness while maximizing the remote state estimation error covariance of the system. Subsequently, the attack strategy is formulated as a convex optimization problem to achieve the maximum estimation error. Finally, two simulation examples, integrated with real-world systems, were utilized to validate the theoretical results.