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

Chao Zhou, Chunhua Wang, Wei Yao and Hairong Lin

Applied Mathematics and Computation, 2022, vol. 425, issue C

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.

Keywords: Synchronization; Memristive neural networks; DoS attacks; Actuator saturation; Image encryption (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:425:y:2022:i:c:s0096300322001643

DOI: 10.1016/j.amc.2022.127080

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