Event-triggered synchronization control for neural networks against DoS attacks

This paper studied the event-triggered synchronization problem for time-delay neural networks under DoS attacks. A novel event-triggered scheme based on switching between periodic sampling and a continuous event-triggered scheme was proposed, which not only cuts down the number of data transmissions...

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Main Authors: Yawei Liu, Guangyin Cui, Chen Gao
Format: Article
Language:English
Published: AIMS Press 2025-01-01
Series:Electronic Research Archive
Subjects:
Online Access:https://www.aimspress.com/article/doi/10.3934/era.2025007
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author Yawei Liu
Guangyin Cui
Chen Gao
author_facet Yawei Liu
Guangyin Cui
Chen Gao
author_sort Yawei Liu
collection DOAJ
description This paper studied the event-triggered synchronization problem for time-delay neural networks under DoS attacks. A novel event-triggered scheme based on switching between periodic sampling and a continuous event-triggered scheme was proposed, which not only cuts down the number of data transmissions but also offsets cyberattacks. By choosing a suitable piecewise Lyapunov-Krasovskii functional and using several free-weighting matrices, sufficient conditions were established to ensure the exponential stability of the synchronization error system in the occurrence of DoS attacks. Furthermore, a co-design method was provided to acquire the desired non-fragile output-feedback control gain and event-triggering parameter. Finally, a numerical example was given to illustrate the usefulness of the proposed approach.
format Article
id doaj-art-ce2b706a488d43eeb4fad9c2c33aac46
institution OA Journals
issn 2688-1594
language English
publishDate 2025-01-01
publisher AIMS Press
record_format Article
series Electronic Research Archive
spelling doaj-art-ce2b706a488d43eeb4fad9c2c33aac462025-08-20T02:26:15ZengAIMS PressElectronic Research Archive2688-15942025-01-0133112114110.3934/era.2025007Event-triggered synchronization control for neural networks against DoS attacksYawei Liu0Guangyin Cui1Chen Gao2Kunlun Digital Technology Co. Ltd., Beijing 100007, ChinaKunlun Digital Technology Co. Ltd., Beijing 100007, ChinaKunlun Digital Technology Co. Ltd., Beijing 100007, ChinaThis paper studied the event-triggered synchronization problem for time-delay neural networks under DoS attacks. A novel event-triggered scheme based on switching between periodic sampling and a continuous event-triggered scheme was proposed, which not only cuts down the number of data transmissions but also offsets cyberattacks. By choosing a suitable piecewise Lyapunov-Krasovskii functional and using several free-weighting matrices, sufficient conditions were established to ensure the exponential stability of the synchronization error system in the occurrence of DoS attacks. Furthermore, a co-design method was provided to acquire the desired non-fragile output-feedback control gain and event-triggering parameter. Finally, a numerical example was given to illustrate the usefulness of the proposed approach.https://www.aimspress.com/article/doi/10.3934/era.2025007event-triggered schemesynchronization controldos attacksneural networks
spellingShingle Yawei Liu
Guangyin Cui
Chen Gao
Event-triggered synchronization control for neural networks against DoS attacks
Electronic Research Archive
event-triggered scheme
synchronization control
dos attacks
neural networks
title Event-triggered synchronization control for neural networks against DoS attacks
title_full Event-triggered synchronization control for neural networks against DoS attacks
title_fullStr Event-triggered synchronization control for neural networks against DoS attacks
title_full_unstemmed Event-triggered synchronization control for neural networks against DoS attacks
title_short Event-triggered synchronization control for neural networks against DoS attacks
title_sort event triggered synchronization control for neural networks against dos attacks
topic event-triggered scheme
synchronization control
dos attacks
neural networks
url https://www.aimspress.com/article/doi/10.3934/era.2025007
work_keys_str_mv AT yaweiliu eventtriggeredsynchronizationcontrolforneuralnetworksagainstdosattacks
AT guangyincui eventtriggeredsynchronizationcontrolforneuralnetworksagainstdosattacks
AT chengao eventtriggeredsynchronizationcontrolforneuralnetworksagainstdosattacks