Model-based adaptive security control strategy against false data injection attacks in cyber–physical systems

Abstract

This paper addresses the problem of adaptive security control for cyber–physical systems (CPSs) under external nonlinear disturbances and false data injection (FDI) attacks on actuators. To address FDI attacks, a full-order disturbance estimator is proposed for eliminating disturbances in the forward channel. Furthermore, an attack model based on the system state vector is developed to estimate the attack vector by considering potential attacks that can induce state errors between the reference model and the actual system. Subsequently, a novel model-based adaptive security control (MASC) strategy is devised using attack compensation and state feedback. The key advantage of our proposed strategy lies in its ability to effectively mitigate the adverse effects of attacks while accounting for nonlinear disturbances. Finally, experimental validation conducted on a brushless DC motor speed control system demonstrates the capability of achieving high output tracking accuracy.