Event-triggered impulsive synchronization control of fractional-order coupled neural networks with deception attacks
Xuelian Li, Haibo Bao · 2024
In this paper, we investigate the synchronization problem of networks subjected to deception attacks. First of all, a class of fractional-order neural network model in which the attacker can launch a malicious attack at any time and change the intensity of the attack is established in this paper. Meanwhile, the Bernoulli distribution is used to characterize the inputs of incorrect data. Secondly, this paper proposes an event-triggered impulsive control method that is vulnerable to deception attacks, which can reduce superfluous data transmission and the likelihood of a node being subjected to malicious attack. The Lyapunov function approach and fractional calculus are then used for providing sufficient conditions to achieve mean-square synchronization while avoiding the Zeno phenomenon. In the end, the correctness of the obtained results is justified by a numerical simulation