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Observer-based synchronization of memristive neural networks under DoS attacks and actuator saturation and its application to image encryption

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  • Zhou, Chao
  • Wang, Chunhua
  • Yao, Wei
  • Lin, Hairong

Abstract

In this article, the synchronization issue of memristive neural networks (MNNs) under denial-of-service (DoS) attacks and actuator saturation is investigated via an observer-based controller. Due to actual physical constraint, the effect of actuator saturation is taken into account in the controller design. Unlike the existing works where the communication environment is secure, DoS attacks are explored in the communication channel connecting master and slave MNNs. Based on the above considerations, an observer-based control approach is developed to estimate the MNNs states and guarantee the MNNs synchronization in the presences of DoS attacks and actuator saturation. By using the Lyapunov method and stochastic analysis technique, the sufficient synchronization conditions are derived via a set of linear matrix inequalities (LMIs). Meanwhile, the attraction domain of error system is estimated to satisfy the demand of actuator saturation. Then, numerical simulation is used to manifest the validity of our theoretical results. Finally, the proposed synchronization theory is applied to image encryption. The experimental results demonstrate that the presented image encryption scheme has a reliable performance.

Suggested Citation

  • Zhou, Chao & Wang, Chunhua & Yao, Wei & Lin, Hairong, 2022. "Observer-based synchronization of memristive neural networks under DoS attacks and actuator saturation and its application to image encryption," Applied Mathematics and Computation, Elsevier, vol. 425(C).
    • Handle: RePEc:eee:apmaco:v:425:y:2022:i:c:s0096300322001643
    DOI: 10.1016/j.amc.2022.127080
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    References listed on IDEAS

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    1. Wang, Weiping & Jia, Xiao & Luo, Xiong & Kurths, Jürgen & Yuan, Manman, 2019. "Fixed-time synchronization control of memristive MAM neural networks with mixed delays and application in chaotic secure communication," Chaos, Solitons & Fractals, Elsevier, vol. 126(C), pages 85-96.
    2. Yao, Wei & Wang, Chunhua & Sun, Yichuang & Zhou, Chao & Lin, Hairong, 2020. "Exponential multistability of memristive Cohen-Grossberg neural networks with stochastic parameter perturbations," Applied Mathematics and Computation, Elsevier, vol. 386(C).
    3. Zhu, Sha & Bao, Haibo, 2022. "Event-triggered synchronization of coupled memristive neural networks," Applied Mathematics and Computation, Elsevier, vol. 415(C).
    4. Feng, Yuming & Yang, Xinsong & Song, Qiang & Cao, Jinde, 2018. "Synchronization of memristive neural networks with mixed delays via quantized intermittent control," Applied Mathematics and Computation, Elsevier, vol. 339(C), pages 874-887.
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    Citations

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    Cited by:

    1. Qifeng Fu & Xuemei Xu & Chuwen Xiao, 2022. "LQR Chaos Synchronization for a Novel Memristor-Based Hyperchaotic Oscillator," Mathematics, MDPI, vol. 11(1), pages 1-16, December.
    2. Yue Zhu & Chunhua Wang & Jingru Sun & Fei Yu, 2023. "A Chaotic Image Encryption Method Based on the Artificial Fish Swarms Algorithm and the DNA Coding," Mathematics, MDPI, vol. 11(3), pages 1-18, February.
    3. Hua, Wentao & Wang, Yantao & Liu, Chunyan, 2024. "New method for global exponential synchronization of multi-link memristive neural networks with three kinds of time-varying delays," Applied Mathematics and Computation, Elsevier, vol. 471(C).
    4. Zhao, Ningning & Qiao, Yuanhua, 2024. "Stability analysis of Clifford-valued memristor-based neural networks with impulsive disturbances and its application to image encryption," Applied Mathematics and Computation, Elsevier, vol. 475(C).
    5. Kiruthika, R. & Krishnasamy, R. & Lakshmanan, S. & Prakash, M. & Manivannan, A., 2023. "Non-fragile sampled-data control for synchronization of chaotic fractional-order delayed neural networks via LMI approach," Chaos, Solitons & Fractals, Elsevier, vol. 169(C).
    6. Hairong Lin & Chunhua Wang & Fei Yu & Jingru Sun & Sichun Du & Zekun Deng & Quanli Deng, 2023. "A Review of Chaotic Systems Based on Memristive Hopfield Neural Networks," Mathematics, MDPI, vol. 11(6), pages 1-18, March.
    7. Fei Yu & Wuxiong Zhang & Xiaoli Xiao & Wei Yao & Shuo Cai & Jin Zhang & Chunhua Wang & Yi Li, 2023. "Dynamic Analysis and FPGA Implementation of a New, Simple 5D Memristive Hyperchaotic Sprott-C System," Mathematics, MDPI, vol. 11(3), pages 1-15, January.

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