Neural Network-Adaptive Secure Control for Nonlinear Cyber-Physical Systems Against Adversarial Attacks

Abstract

The “insecurity of the network” characterizes each agent as being remotely controlled through unreliable network channels. In such an insecure network, the output signal can be altered through carefully designed adversarial attacks to produce erroneous results. To address this, this paper proposes a neural network (NN) adaptive secure control scheme for cyber-physical systems (CPSs) via attack reconstruction strategies, where the attack reconstruction strategy serves as the solution to the NNs estimation problem on the insecurity of the network. Consequently, by introducing a novel error transformation, an NN-adaptive secure control method is formulated as the framework of backstepping. Based on the Lyapunov stability theory and defined error transformation, it is proven that the above secure control process reaches the expected trajectory, and all the signals are bounded in closed-loop systems. Finally, its effectiveness is verified via a simulation of attitude control of two-joint robots.

Keywords

• cyber-physical systems
• neural networks
• adversarial attacks
• secure control
• error transformation