Quasi-synchronization of stochastic memristive neural networks subject to deception attacks

In this paper, the quasi-synchronization problem of stochastic memristive neural networks (MNNs) subject to deception attacks is investigated via hybrid impulsive control. Deception attacks in the MNN synchronization model, which involve the attacker attempting to inject some false data into sensor-...

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Veröffentlicht in:Nonlinear dynamics Jg. 111; H. 3; S. 2443 - 2462
Hauptverfasser: Chao, Zhou, Wang, Chunhua, Yao, Wei
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Dordrecht Springer Netherlands 01.02.2023
Springer Nature B.V
Schlagworte:
Automotive Engineering
Classical Mechanics
Control
Deception
Design parameters
Dynamical Systems
Engineering
Mechanical Engineering
Neural networks
Original Paper
Synchronism
Vibration
Quasi-synchronization
Memristive neural networks
Stochastic disturbance
Impulsive differential inequality
Deception attack
ISSN:0924-090X, 1573-269X
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Zusammenfassung:In this paper, the quasi-synchronization problem of stochastic memristive neural networks (MNNs) subject to deception attacks is investigated via hybrid impulsive control. Deception attacks in the MNN synchronization model, which involve the attacker attempting to inject some false data into sensor-to-controller channels to destroy the control signal, are investigated from the perspective of network communication security. The attack conditions are described using stochastic variables that obey the Bernoulli distribution. Inspired by existing impulsive differential inequalities, a new inequality is proposed, which is useful for dealing with quasi-synchronization in impulsive systems. Thereafter, sufficient conditions and the error bound are obtained for validating the quasi-synchronization of stochastic MNNs subject to deception attacks based on the proposed inequality and Lyapunov stability theory. In the absence of an attack, the globally complete synchronization problem for stochastic MNNs is investigated. Additionally, the attack effects and their mitigation through control parameter design are discussed. Finally, the simulation results are presented to validate the theoretical analysis.
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ISSN:0924-090X
1573-269X
DOI:10.1007/s11071-022-07925-2