Security control of Markovian jump neural networks with stochastic sampling subject to false data injection attacks★ ★This work was supported by the NNSF of China under Grants 61973199, 62003794, 62173214, the Shandong Provincial NSF ZR2020QF050, ZR2021MF003.

Abstract

The security control of Markovian jumping neural networks (MJNNs) is investigated under false data injection attacks that take place in the shared communication network. Stochastic sampled-data control is employed to research the exponential synchronization of MJNNs under false data injection attacks (FDIAs) since it can alleviate the impact of the FDIAs on the performance of the system by adjusting the sampling periods. A multi-delay error system model is established through the input-delay approach. To reduce the conservatism of the results, a sampling-period-probability-dependent looped Lyapunov functional is constructed. In light of some less conservative integral inequalities, a synchronization criterion is derived, and an algorithm is provided that can be solved for determining the controller gain. Finally, a numerical simulation is presented to confirm the efficiency of the proposed method.