Cyber-Attack Detection in Discrete-Time Nonlinear Multi-Agent Systems Using Neural Networks

This paper proposes a distributed cyber-attack detection method in communication channels for a class of discrete-time, nonlinear, heterogeneous, multi-agent systems controlled by the proposed formation-based controller. To detect false data injection attacks on agents communication channels, each agent exploits a residual-based detection system equipped with a neural network (NN)-based observer. Moreover, the NN weights tuning law and the attack detectability threshold are derived using a Lyapunov function. The uniform ultimate boundedness (UUB) of the formation error and detector residual are proven based on the Lyapunov stability theory. Finally, the attack detectability condition of the proposed method is analyzed, and a simulation example is provided to demonstrate the performance of the proposed detection methodology.