Adaptive FDI Attack Detection in Nonlinear Cyber‐Physical Systems With Uncertain Parameters

This article focuses on the detection of false data injection (FDI) attacks in nonlinear cyber‐physical systems with uncertain parameters, aiming to improve both the speed and accuracy of actuator detection affected by FDI attacks. First, considering the uncertainties or errors often present in practical cyber‐physical system models, two FDI attack detection estimator algorithms with different levels of conservatism are proposed to enhance detection accuracy. Second, compared to traditional detection thresholds, a novel adaptive detection threshold is introduced to improve the speed of attack detection. Third, to address the issue of limited communication bandwidth, transmission signals are quantized. Finally, the applicability and effectiveness of the proposed method are validated through two simulation examples.