DFA-mode-dependent stability of impulsive switched memristive neural networks under channel-covert aperiodic asynchronous attacks.

Journal: Neural networks : the official journal of the International Neural Network Society
Published Date: Dec 1, 2024

Abstract

This article is concerned with the deterministic finite automaton-mode-dependent (DFAMD) exponential stability problem of impulsive switched memristive neural networks (SMNNs) with aperiodic asynchronous attacks and the network covert channel. First, unlike the existing literature on SMNNs, this article focuses on DFA to drive mode switching, which facilitates precise system behavior modeling based on deterministic rules and input characters. To eliminate the periodicity and consistency constraints of traditional attacks, this article presents the multichannel aperiodic asynchronous denial-of-service (DoS) attacks, allowing for the diversity of attack sequences. Meanwhile, the network covert channel with a security layer is exploited and its dynamic adjustment is realized jointly through the dynamic weighted try-once-discard (DWTOD) protocol and selector, which can reduce network congestion, improve data security, and enhance system defense capability. In addition, this article proposes a novel mode-dependent hybrid controller composed of output feedback control and mode-dependent impulsive control, with the goal of increasing system flexibility and efficiency. Inspired by the semi-tensor product (STP) technique, Lyapunov-Krasovskii functions, and inequality technology, the novel exponential stability conditions are derived. Finally, a numerical simulation is provided to illustrate the effectiveness of the developed approach.

Authors

  • Xinyi Han
    School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, Sichuan, China. Electronic address: xinyihan@std.uestc.edu.cn.
  • Yongbin Yu
    School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, 610054, China. Electronic address: ybyu@uestc.edu.cn.
  • Xiangxiang Wang
    School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, 610054, China.
  • Xiao Feng
    College of Computer, Chengdu University, Chengdu, China.
  • Jingya Wang
    School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, Sichuan, China. Electronic address: jingya.wang@std.uestc.edu.cn.
  • Jingye Cai
    School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, 610054, China.
  • Kaibo Shi
    School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, PR China. Electronic address: skbs111@163.com.
  • Shouming Zhong
    School of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, PR China; Key Laboratory for Neuroinformation of Ministry of Education, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, PR China.

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