Joint Estimation of Deception Attacks on Sensors and Actuators in Cyber–Physical Systems

This article presents a strategy for estimating deception false data injection (FDI) attacks on sensors and actuators in cyber–physical systems (CPSs) described by a class of linear parameter-varying (LPV) systems. Gain-scheduled intermediate-variable-based estimators (GSIVBEs) are proposed to reconstruct both the system states and the attack sequences, ensuring bounded estimation errors even in cases where such attacks evade detection by conventional threshold-based methods. A convex design condition is derived to ensure that the estimation error dynamics are input-to-state stable with respect to variations in the attack sequences. To improve the accuracy of the estimators, an optimization problem is formulated to minimize the estimation error under these conditions. In addition, different strategies are proposed to mitigate the effects of the attack sequences on the system by using the estimated attack sequences as part of an attack-tolerant control framework. The proposed approach demonstrates its effectiveness in enhancing system resilience, contributing to the security and reliability of CPSs under adversarial scenarios.