Secure Control of Markovian Jumping Systems Under Deception Attacks: An Attack-Probability-Dependent Adaptive Event-Triggered Mechanism

Yao, Lan; Huang, Xia; Wang, Zhen; Liu, Kun

doi:10.1109/tcns.2023.3269007

Cited by 4 publications

(4 citation statements)

References 41 publications

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“…The consensus or synchronization problem has always been a central issue in MASs, aiming to achieve agreement among multiagents through communication. The issue of consensus in MASs has been extensively explored, including H ∞ consensus [4,5], event-triggered consensus [6][7][8], distributed consensus [9], finite/fixed-time consensus [10,11], and so on.…”

Section: Introductionmentioning

confidence: 99%

“…Signal quantization is a prevalent control strategy in digital communication for optimizing network communication resources. In practical applications, the eventtriggering mechanisms (ETMs) [6][7][8]11,[26][27][28][29] are instrumental in lowering communication burdens among multiagents and conserving network resources. Building upon this, MAS consensus has been studied by combining quantization strategies and ETMs.…”

Section: Introductionmentioning

confidence: 99%

“…If the quantizer q i ψ(k) (v i ) = v i , then the QETM can degenerate into the dynamic eventtriggered mechanism [7,11,26].…”

mentioning

confidence: 99%

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Quantization-Based Event-Triggered H∞ Consensus for Discrete-Time Markov Jump Fractional-Order Multiagent Systems with DoS Attacks

Lu,

Wu,

Wang

et al. 2024

Fractal Fract

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This paper investigates the H∞ consensus problem of discrete-time Markov jump fractional-order multiagent systems (DTMJFOMASs) under denial-of-service (DoS) attacks. By applying the short-memory principle, we can obtain discrete-time Markov jump multiagent systems with partially unknown probabilities. A novel quantized event-triggering mechanism (QETM), based on a mode-dependent logarithmic quantizer, is proposed to enhance transmission efficiency among multiagents. A distributed controller with quantized output is developed. Sufficient conditions are provided to ensure the system achieves H∞ consensus through Lyapunov stability theory. Finally, two examples are given to verify the effectiveness of the proposed model.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Quantization-Based Event-Triggered H∞ Consensus for Discrete-Time Markov Jump Fractional-Order Multiagent Systems with DoS Attacks

Lu,

Wu,

Wang

et al. 2024

Fractal Fract

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“…Therefore, the security control problem of SDC systems has been widely discussed by researchers. There are many types of cyber attacks, mainly including deception attacks [18][19][20][21], DoS attacks [22], and false data injection attacks (FDIA) [23][24][25]. Therein, FDIA, as a common type of attack, can destroy the integrity of communication data by sending error information to the receiver, thus causing unpredictable damage to the SDC system.…”

Section: Introductionmentioning

confidence: 99%

Security control of Markovian jump neural networks with stochastic sampling subject to false data injection attacks★

Yao

Huang

Wang³

et al. 2023

Commun. Theor. Phys.

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The security control of Markovian jumping neural networks (MJNNs) is investigated under false data injection attacks that taken place in the shared communication network. Stochastic sampled data control (SSDC) is employed to research the exponential synchronization of MJNNs under false data injection attacks (FDIAs) since it can alleviate the impact of the FDIAs on the performance of the system by adjusting the sampling periods. A multi-delays error system model is established through the input-delay approach. To reduce the conservatism of the results, a sampling-period-probability-dependent looped Lyapunov functional (LLF) is constructed. In light of some less conservative integral inequalities, a synchronization criterion is derived, and an algorithm is provided that can be solved for determining the controller gain. Finally, a numerical simulation is presented to confirm the efficiency of the proposed method.

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Event-triggering adaptive neural network output feedback control for networked systems under false data injection attacks

Zhao,

Shi,

Zhang

et al. 2024

Chaos, Solitons & Fractals

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Secure Control of Markovian Jumping Systems Under Deception Attacks: An Attack-Probability-Dependent Adaptive Event-Triggered Mechanism

Cited by 4 publications

References 41 publications

Quantization-Based Event-Triggered H∞ Consensus for Discrete-Time Markov Jump Fractional-Order Multiagent Systems with DoS Attacks

Quantization-Based Event-Triggered H∞ Consensus for Discrete-Time Markov Jump Fractional-Order Multiagent Systems with DoS Attacks

Security control of Markovian jump neural networks with stochastic sampling subject to false data injection attacks★

Event-triggering adaptive neural network output feedback control for networked systems under false data injection attacks

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