Event-triggered filter design for nonlinear cyber–physical systems subject to deception attacks

Gu, Zhou; Zhou, Xiaohong; Zhang, Tao; Yang, Fan; Shen, Mouquan

doi:10.1016/j.isatra.2019.02.036

Cited by 85 publications

(37 citation statements)

References 28 publications

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“…Recently, owing to the openness of communication channel, cyber-attacks have become the major threat to network security [42]- [44]. Filtering problems considering the impact of different cyberattacks have thus drawn increasing attention [45]- [47]. However, to the authors' knowledge, no results are available in the literature on the hybrid and adaptive ETCM-based filtering for nonlinear Makov jump systems with cyber-attacks, which is the motivation of this paper.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Event-Triggered Filtering for Nonlinear Markov Jump Systems With Stochastic Cyber-Attacks

et al. 2021

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Section: Introductionmentioning

confidence: 99%

Hybrid Event-Triggered Filtering for Nonlinear Markov Jump Systems With Stochastic Cyber-Attacks

et al. 2021

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“…Since the advantage of saving limited communication resources, event-triggered scheme (ETS) become an effective manner to reduce communication cost without losing much system performance [12][13][14][15][16]. By using event-triggered scheme, many outcomes of MJSs about robust control [17], quantized control [18] and finite-time control [19] have been reported.…”

Section: Introductionmentioning

confidence: 99%

Co-Design of Event-Triggered Scheme and H_∞ Output Control for Markov Jump Systems Against Deception Attacks

Yan

Nguang

et al. 2020

IEEE Access

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This paper is concerned with the co-design problem of event-triggered scheme and H ∞ static output control of linear Markov jump systems with deception attacks. To save the previous communication resources, a mode-dependent event-triggered scheme is utilized based on system output. To describe the deception attacks, a random variable satisfying Bernoulli distribution is employed. By using a separation approach, sufficient linear matrix inequality conditions for the existence of event-triggered output controllers that ensure the stochastic stability with prescribed H ∞ are obtained. Then, a co-design algorithm is proposed to obtain the trade-off between the communication cost and H ∞ performance. Lastly, the validity of the developed method is verified by a numerical example and a practical single-link robot arm system.

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“…Cyber attacks are classified into three types: repeated attack, denial of service attack, and deception attack, in which the biggest threat to network security is deception attacks. Some interesting results associated with deception attacks have been given [31][32][33][34]. For instance, a secure filter is devised for the delayed stochastic nonlinear systems with a novel multiple-channel attack model [33].…”

Section: Introductionmentioning

confidence: 99%

“…For instance, a secure filter is devised for the delayed stochastic nonlinear systems with a novel multiple-channel attack model [33]. For a nonlinear physical system subject to data injection attacks, an elastic filter is designed by employing a new ETM in reference [34]. e goal of [35] is to devise a filter which can guarantee the system security in the presence of stochastic sensor saturation and stochastic deception attacks.…”

Section: Introductionmentioning

confidence: 99%

Event-Triggered $H_{\infty}$ Filtering for Markovian Jump Neural Networks under Random Missing Measurements and Deception Attacks

et al. 2020

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This paper concentrates on the event-triggered H ∞ filter design for the discrete-time Markovian jump neural networks under random missing measurements and cyber attacks. Considering that the controlled system and the filtering can exchange information over a shared communication network which is vulnerable to the cyber attacks and has limited bandwidth, the event-triggered mechanism is proposed to relieve the communication burden of data transmission. A variable conforming to Bernoulli distribution is exploited to describe the stochastic phenomenon since the missing measurements occur with random probability. Furthermore, seeing that the communication networks are vulnerable to external malicious attacks, the transferred information via the shared communication network may be changed by the injected false information from the attackers. Based on the above consideration, sufficient conditions for the filtering error system to maintain asymptotically stable are provided with predefined H ∞ performance. In the end, three numerical examples are given to verify the proposed theoretical results.

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Event-triggered filter design for nonlinear cyber–physical systems subject to deception attacks

Cited by 85 publications

References 28 publications

Hybrid Event-Triggered Filtering for Nonlinear Markov Jump Systems With Stochastic Cyber-Attacks

Hybrid Event-Triggered Filtering for Nonlinear Markov Jump Systems With Stochastic Cyber-Attacks

Co-Design of Event-Triggered Scheme and H_∞ Output Control for Markov Jump Systems Against Deception Attacks

Event-Triggered $H_{\infty}$ Filtering for Markovian Jump Neural Networks under Random Missing Measurements and Deception Attacks

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Event-triggered filter design for nonlinear cyber–physical systems subject to deception attacks

Cited by 85 publications

References 28 publications

Hybrid Event-Triggered Filtering for Nonlinear Markov Jump Systems With Stochastic Cyber-Attacks

Hybrid Event-Triggered Filtering for Nonlinear Markov Jump Systems With Stochastic Cyber-Attacks

Co-Design of Event-Triggered Scheme and H∞ Output Control for Markov Jump Systems Against Deception Attacks

Event-Triggered H ∞ Filtering for Markovian Jump Neural Networks under Random Missing Measurements and Deception Attacks

Contact Info

Product

Resources

About

Co-Design of Event-Triggered Scheme and H_∞ Output Control for Markov Jump Systems Against Deception Attacks

Event-Triggered $H_{\infty}$ Filtering for Markovian Jump Neural Networks under Random Missing Measurements and Deception Attacks