Input-Retention Strategies for Secure Synchronization of Piecewise Markov Neural Networks Under Hybrid Cyber-Attacks

Cao, Y; Zhao, S; Wen, S; Huang, T

Input-Retention Strategies for Secure Synchronization of Piecewise Markov Neural Networks Under Hybrid Cyber-Attacks

Cao, Y Zhao, S

Wen, S

Huang, T

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Transactions on Circuits and Systems I Regular Papers, 2025, PP, (99)
Issue Date:: 2025-01-01

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Field	Value	Language
dc.contributor.author	Cao, Y
dc.contributor.author	Zhao, S https://orcid.org/0000-0003-4574-3062
dc.contributor.author	Wen, S https://orcid.org/0000-0001-8077-7001
dc.contributor.author	Huang, T
dc.date.accessioned	2025-07-11T02:19:52Z
dc.date.available	2025-07-11T02:19:52Z
dc.date.issued	2025-01-01
dc.identifier.citation	IEEE Transactions on Circuits and Systems I Regular Papers, 2025, PP, (99)
dc.identifier.issn	1549-8328
dc.identifier.issn	1558-0806
dc.identifier.uri	http://hdl.handle.net/10453/188171
dc.description.abstract	How to achieve synchronization control for Piecewise Homogeneous Markov Delayed Neural Networks (PHMDNNs) under hybrid cyber-attacks is the primary focus of this research. Firstly, a Piecewise Homogeneous Markov Process (PHMP) is employed to model the mode transitions of system parameters and controllers, accurately capturing the dynamic characteristics of practical systems and providing a solid foundation for subsequent controller design. In response to the challenges arising from hybrid cyber-attacks, a novel controller is developed based on an input retention strategy. This ensures system stability under hybrid cyber-attacks, effectively avoiding the instability issues caused by traditional zero-input strategies and enhancing control robustness. To further optimize system performance, an improved Resilient Adaptive Event-triggered Mechanism (RAETM) is proposed. By optimizing triggering conditions and thresholds, the mechanism reduces communication overhead while strengthening system security, making it well-suited for networked control systems. In addition, a generalized common Lyapunov functional is constructed by incorporating sampling instants, time delays, and Markov jump parameters. Sufficient conditions for system synchronization and stability are derived, providing a simplified analytical framework. Finally, the effectiveness and superiority of the proposed approach are confirmed through simulation results, showcasing its robust performance against hybrid cyber-attacks and its ability to achieve secure synchronization.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Transactions on Circuits and Systems I Regular Papers
dc.relation.isbasedon	10.1109/TCSI.2025.3559210
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0906 Electrical and Electronic Engineering
dc.subject.classification	Electrical & Electronic Engineering
dc.subject.classification	4009 Electronics, sensors and digital hardware
dc.title	Input-Retention Strategies for Secure Synchronization of Piecewise Markov Neural Networks Under Hybrid Cyber-Attacks
dc.type	Journal Article
utslib.citation.volume	PP
utslib.for	0906 Electrical and Electronic Engineering
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	University of Technology Sydney/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/Australian Artificial Intelligence Institute (AAII)
utslib.copyright.status	open_access	*
dc.date.updated	2025-07-11T02:19:51Z
pubs.issue	99
pubs.publication-status	Published
pubs.volume	PP
utslib.citation.issue	99

Abstract:

How to achieve synchronization control for Piecewise Homogeneous Markov Delayed Neural Networks (PHMDNNs) under hybrid cyber-attacks is the primary focus of this research. Firstly, a Piecewise Homogeneous Markov Process (PHMP) is employed to model the mode transitions of system parameters and controllers, accurately capturing the dynamic characteristics of practical systems and providing a solid foundation for subsequent controller design. In response to the challenges arising from hybrid cyber-attacks, a novel controller is developed based on an input retention strategy. This ensures system stability under hybrid cyber-attacks, effectively avoiding the instability issues caused by traditional zero-input strategies and enhancing control robustness. To further optimize system performance, an improved Resilient Adaptive Event-triggered Mechanism (RAETM) is proposed. By optimizing triggering conditions and thresholds, the mechanism reduces communication overhead while strengthening system security, making it well-suited for networked control systems. In addition, a generalized common Lyapunov functional is constructed by incorporating sampling instants, time delays, and Markov jump parameters. Sufficient conditions for system synchronization and stability are derived, providing a simplified analytical framework. Finally, the effectiveness and superiority of the proposed approach are confirmed through simulation results, showcasing its robust performance against hybrid cyber-attacks and its ability to achieve secure synchronization.

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http://hdl.handle.net/10453/188171