Sampled-Data-Based Secure Synchronization Control of Delayed Coupled Fuzzy Inertial Neural Networks Under Deception Attacks

This article investigates the security control issue of delayed coupled fuzzy inertial neural networks (FINNs) under deception attacks. Aiming to alleviate the influence of deception attacks, a fuzzy sampling data security controller is designed. A theoretical structure is formulated to analyze the behavior of the closed-loop system under deceptive interference. On this basis, by constructing a suitable set of Lyapunov functionals (LKFs) and employing inequality techniques, criteria guaranteeing exponential synchronization are established using linear matrix inequalities (LMIs). Finally, the effectiveness of the proposed method is demonstrated via numerical simulations and encryption and decryption analysis. Results show that, affected by deception attacks, the coupling FINNs can achieve exponential synchronization through our developed security control approach.