Event-triggered synchronization for delayed reaction–diffusion neural networks under hybrid deception attacks

This paper focuses on designing some event-triggered mechanisms to synchronize the delayed reaction–diffusion neural networks (DRDNNs) under hybrid stochastic deception attacks. Firstly, two different kinds of stochastic deception attacks are considered for DRDNNs system. Based on the distinct characteristics of the attack signals, a novel adaptive event-triggered control mechanism is proposed to synchronize DRDNNs under hybrid deception attacks subject to Lipschitz form, and a novel aperiodic event-triggered control mechanism is proposed to synchronize DRDNNs under hybrid deception attacks subject to bounded form. Using the Lyapunov theory and some inequality techniques, several event-triggered synchronization criteria for DRDNNs under hybrid deception attacks are established. Furthermore, Zeno behavior is strictly excluded by proving the existence of lower bounds for the intervals between any adjacent events in the two designed event-triggered mechanisms. Finally, two numerical examples are provided to illustrate the effectively and superiority of the two proposed event-triggered mechanisms.