Stability of Saturated Memristive Neural Networks Under Probabilistic Deception Attacks

HAN Xin-yi; YU Yong-bin; WANG Xiang-xiang; CAI Jing-ye; FENG Xiao; WANG Jing-ya; ZHONG Shou-ming

doi:10.3969/j.issn.2096-6091.2023.22.002

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Stability of Saturated Memristive Neural Networks Under Probabilistic Deception Attacks

RESEARCH PAPER | 更新时间：2025-03-26

- Stability of Saturated Memristive Neural Networks Under Probabilistic Deception Attacks
- New Generation of Information Technology Vol. 6, Issue 22, Pages: 10-19(2023)
- 作者机构：
  
  1.电子科技大学信息与软件工程学院，四川成都 610054
  2.电子科技大学数学科学学院，四川成都 611731
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.2096-6091.2023.22.002
  CLC： TP13
- Published：30 November 2023
- 稿件说明：
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韩欣佚, 于永斌, 王向向, 等. 概率欺骗攻击下的饱和忆阻神经网络稳定性[J]. 新一代信息技术, 2023, 6(22): 10-19

HAN Xin-yi, YU Yong-bin, WANG Xiang-xiang, et al. Stability of Saturated Memristive Neural Networks Under Probabilistic Deception Attacks[J]. New Generation of Information Technology, 2023, 6(22): 10-19
韩欣佚, 于永斌, 王向向, 等. 概率欺骗攻击下的饱和忆阻神经网络稳定性[J]. 新一代信息技术, 2023, 6(22): 10-19 DOI： 10.3969/j.issn.2096-6091.2023.22.002.

HAN Xin-yi, YU Yong-bin, WANG Xiang-xiang, et al. Stability of Saturated Memristive Neural Networks Under Probabilistic Deception Attacks[J]. New Generation of Information Technology, 2023, 6(22): 10-19 DOI： 10.3969/j.issn.2096-6091.2023.22.002.

摘要

本文聚焦一类饱和忆阻神经网络模型，利用攻击驱动的事件触发控制策略，研究了其在概率欺骗攻击下的均方稳定问题。首先，针对器件物理限制或设备损耗导致的执行器饱和现象，本文建立了饱和忆阻神经网络模型，解决了执行器与控制器间的信号传输存在较大偏差造成的控制失效问题。其次，网络的强开放性和不可预测性使信号在传输时容易受到攻击，本文结合伯努利过程设计了具有时变时延的概率欺骗攻击模型和攻击驱动的事件触发控制策略，提升了系统应对复杂环境的抗干扰能力，并且减少了数据传输量，节省了通信资源。此外，本文构建了一种依赖于事件触发时刻的新型李雅普诺夫泛函，加强了对系统状态信息的利用，并通过李雅普诺夫稳定性理论和不等式技术，推导出系统均方稳定的充分条件。最后，通过数值仿真验证了结果的可行性和有效性。

Abstract

This paper focuses on a class of saturated memristive neural network models and investigates its mean square stable problem under probabilistic deception attacks with an attack-driven event-triggered control strategy. First

aiming at the actuator saturation phenomenon under device physical limitations or equipment loss

this paper establishes the saturated memristive neural networks

which resolve the issue of control failure caused by significant deviations in signal transmission between actuators and controllers. Next

the strong openness and unpredictability of the network make signals susceptible to attacks during transmission

this paper combines Bernoulli processes to design a probabilistic deception attack model with time-varying delays and an attack-driven event-triggered control strategy

which enhances the anti-interference ability of the system in complex environments

reduces data transmission

and conserves communication resources. Furthermore

this paper constructs a novel Lyapunov functional dependent on event-triggered instants

reinforcing the utilization of system state information. Then

through Lyapunov stability theory and inequality techniques

sufficient conditions for the mean square stability of the system are derived. Ultimately

the feasibility and effectiveness of the results are verified through numerical examples.

关键词

Keywords

references

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