Cyber-Attack Detection in Discrete Nonlinear Multi-Agent Systems Using Neural Networks (2102.01166v1)

Abstract: This paper proposes a distributed cyber-attack detection method in communication channels for a class of discrete, nonlinear, heterogeneous, multi-agent systems that are controlled by our proposed formation-based controller. A residual-based detection system, exploiting a neural network (NN)-based observer, is developed to detect false data injection attacks on agents communication channels. A Lyapunov function is used to derive the NN weights tuning law and the attack detectability threshold. The uniform ultimate boundedness (UUB) of the detector residual and formation error is proven based on the Lyapunov stability theory. The proposed methods attack detectability properties are analyzed, and simulation results demonstrate the proposed detection methodologys performance.