Complex Dynamic Networks for Multiple Attacks: A Jump-Like Event-Triggered Controller Based on Neural Network Model
Jianan Zhang, Yuechao Ma
Abstract
This article designs a jump-like event-triggered control based on the neural network model for complex dynamic networks (CDNs) under multiple attacks. First, the multiple cyberattack model is constructed by considering the aperiodic denial of service (DoS) attacks and deception attacks. In this regard, DoS attacks that limit the frequency and duration of different nodes and deception attacks that eliminate strict assumptions are studied. Second, a novel jump-like event-triggered controller based on neural network is proposed. It can not only adaptively jump between different threshold parameters based on whether DoS attacks occur, but also use weighted neural network to approximate and reduce the adverse influence from deception attacks. Next, by establishing the Lyapunov function and utilizing the integral inequality method, the sufficient conditions for ensuring the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${H_\infty }$</tex-math></inline-formula> exponential synchronization of the system are obtained. Ultimately, the effectiveness of the proposed results are demonstrated through the Chua's circuit.