Adaptive Security Control Against False Data Injection Attacks in Cyber-Physical Systems

In this paper, for a class of nonlinear cyber-physical systems (CPSs), the problem of adaptive security control for false data injection (FDI) attacks on the sensor and actuator is solved. Both sensor and actuator are destroyed by attackers, which makes the feedback control design unable to access the traditional error surface. Firstly, a state observer is constructed to mitigate the impact of sensor attacks. And then neural networks are used to approximate the nonlinear term and compensate for state-dependent actuator attacks. Finally, in order to reduce the impact of FDI attacks, we design a special time-varying symmetry barrier function in backstepping control design, which can achieve specific output signal constraints under FDI attacks. Through the above control strategy, the output constraint control problem of CPSs under FDI attacks is solved. Finally, a numerical simulation example demonstrates the effectiveness of the proposed controller.