Artificial Intelligence for Detection, Estimation, and Compensation of Malicious Attacks in Nonlinear Cyber Physical Systems and Industrial IoT

This paper proposes a hybrid intelligent-classic control approach for reconstruction and compensation of cyber attacks launched on inputs of nonlinear cyber-physical systems (CPS) and industrial IoT systems which work through shared communication networks. In this study, a class of n-order nonlinear systems is considered as a model of CPS while it is in presence of cyber attacks only in the forward channel. An intelligent-classic control system is developed to compensate cyber-attacks. Neural network (NN) is designed as an intelligent estimator for attack estimation and a classic nonlinear control system based on the variable structure control method is designed to compensate the effect of attacks and control the system performance in tracking applications. In the proposed strategy, nonlinear control theory is applied to guarantee the stability of the system when attacks happen. In this strategy, a Gaussian radial basis function NN is used for online estimation and reconstruction of cyber-attacks launched on the networked system. An adaptation law of the intelligent estimator is derived from a Lyapunov function. Simulation results demonstrate the validity and feasibility of the proposed strategy in car cruise control application as the testbed.