Master-Slave Synchronization of Neural Networks Subject to Mixed-Type Communication Attacks

Abstract This paper concerns the master-slave synchronization issue of neural networks subject to mixed-type communication attacks. The synchronization strategy is based on static output feedback controller followed by an event-triggered scheme. The communication network is assumed to be under various types of cyber-attacks, namely, deception, replay, and denial-of-service attacks. All these attacks are investigated in a unified Markovian jump framework. Using the Lyapunov-Krasovskii theory and stochastic analysis techniques, some design criteria are derived and formulated in terms of matrix inequalities. A convex optimization algorithm is proposed to design the static output feedback controller. Finally, two chaotic examples are presented to demonstrate the effectiveness of the event-triggered static output feedback controller.