This article is concerned with the design problem of an $H_{\infty }$ optimal fault detection (FD) filter for networked interval type-2 (IT2) fuzzy systems that are subjected to stochastic cyberattacks. To effectively reduce the utilization of constrained network resources, a new dynamically adjusted event-triggered weighted try-once-discard (DAET-WTOD) protocol is developed, in which two adaptive rules are constructed based on the measured output and the probability of denial-of-service (DoS) attacks. Furthermore, a fuzzy switched-like FD filter is designed with the purpose of detecting system fault signals, while simultaneously considering the DAET-WTOD protocol and stochastic cyberattacks. Subsequently, by utilizing an imperfect premise matching (IPM) scheme, an opposition-based learning adaptive differential evolution algorithm is proposed to deal with the networked IT2 fuzzy systems. This algorithm is capable of iteratively searching the membership function values of the fuzzy filter in real time, thereby achieving improved $H_{\infty }$ performance. Finally, some simulation results are provided to verify the feasibility and advantages of the proposed $H_{\infty }$ optimal FD technique.