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

IF 6 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Neural Networks Pub Date : 2025-03-01 Epub Date: 2024-12-01 DOI:10.1016/j.neunet.2024.106962
Xinyi Han, Yongbin Yu, Xiangxiang Wang, Xiao Feng, Jingya Wang, Jingye Cai, Kaibo Shi, Shouming Zhong
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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.

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信道隐蔽非周期异步攻击下脉冲交换记忆神经网络的dfa模式依赖稳定性。
研究具有非周期异步攻击和网络隐蔽信道的脉冲开关记忆神经网络(smnn)的确定性有限自动机模式相关(DFAMD)指数稳定性问题。首先,与现有关于smnn的文献不同,本文侧重于DFA来驱动模式切换,这有助于基于确定性规则和输入字符的精确系统行为建模。为了消除传统攻击的周期性和一致性约束,本文提出了多通道非周期异步拒绝服务(DoS)攻击,允许攻击序列的多样性。同时,利用具有安全层的网络隐蔽通道,通过动态加权尝试丢弃(DWTOD)协议和选择器共同实现隐蔽通道的动态调整,减少网络拥塞,提高数据安全性,增强系统防御能力。此外,本文还提出了一种由输出反馈控制和模态依赖脉冲控制组成的新型模态依赖混合控制器,以提高系统的灵活性和效率。在半张量积(STP)技术、Lyapunov-Krasovskii函数和不等式技术的启发下,导出了新的指数稳定性条件。最后,通过数值仿真验证了该方法的有效性。
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来源期刊
Neural Networks
Neural Networks 工程技术-计算机:人工智能
CiteScore
13.90
自引率
7.70%
发文量
425
审稿时长
67 days
期刊介绍: Neural Networks is a platform that aims to foster an international community of scholars and practitioners interested in neural networks, deep learning, and other approaches to artificial intelligence and machine learning. Our journal invites submissions covering various aspects of neural networks research, from computational neuroscience and cognitive modeling to mathematical analyses and engineering applications. By providing a forum for interdisciplinary discussions between biology and technology, we aim to encourage the development of biologically-inspired artificial intelligence.
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