• Introduces a sum-based dynamic discrete event-triggered mechanism (SDDETM) that leverages past sampled measurements and internal dynamic variables to reduce network congestion and trigger frequency.
• Models deception attacks via a Bernoulli process, representing a general Markov process, to robustly handle various attack scenarios.
• Co-designs a dynamic output feedback controller (DOFC) with the SDDETM parameters using the cone complement linearization (CCL) algorithm, ensuring system stability.
• Validates the algorithm through two simulation examples, demonstrating effectiveness in synchronization of delayed T–S fuzzy neural networks under deception attacks.