Event-Triggered Dissipative Filtering for Stochastic Fuzzy Complex Networks Under Multiple Cyberattacks

This article investigates the problem of event-based strictly dissipative filtering for stochastic fuzzy complex networks (SFCNs) subject to multiple cyberattacks, including deception attacks and denial-of-service (DoS) attacks. A resilient event-triggered mechanism (ETM) is proposed to reduce the communication burden while effectively countering these cyberattacks. The membership functions are assumed to be mismatched due to the impact of the network environment and sampling behavior. Furthermore, the system dynamics are modeled using Itô-type stochastic differential equations, which include deterministic fuzzy complex networks (CNs) as a special case. Unlike previous studies on stochastic systems, the auxiliary vector function method is employed to introduce more time-varying delay information into the piecewise Lyapunov–Krasovskii functional (LKF), thus reducing conservatism. Consequently, a series of delay-dependent sufficient conditions is derived to ensure the exponentially mean-square stability (EMSS) and strict dissipativity of the filtering error system. Finally, the effectiveness of the proposed method is demonstrated through an illustrative example.