Event-triggered finite-time H∞ filtering for networked systems under deception attacks

Wu, Jian-Cun; Chen, Peng; Zhang, Jin; Zhang, Baolin

doi:10.1016/j.jfranklin.2019.09.002

Cited by 51 publications

(21 citation statements)

References 33 publications

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“…Theorem 2. For given positive scalars 𝛼, 𝛽, s, h, 𝜇 i , 𝜔, 𝜑 1 , T and matrices K 𝜙 , F 𝜙 , R 𝜙 , J i > 0, the augmented system (11) and ( 12) is H ∞ FTB with (𝜑 1 , 𝜑 2 , T, J i , 𝛾, 𝜔) if there exist positive scalars 𝜚 p (p = 1, 2, 3, 4, 5), 𝜆, 𝜑 2 , 𝛾 and matrices…”

Section: Resultsmentioning

confidence: 99%

“…The modes of the system and estimator are given in Figure 1. Under the obtained triggering matrices and the estimator gains in (48), the state response is shown in Figure 2, from which one can see that the augmented system (11) and ( 12) is H ∞ FTB. Under the AETS, the transmitting instants of modes 1-3 are illustrated in Figure 3, 13% sampled instants are released by the event generator.…”

Section: Numerical Examplesmentioning

confidence: 99%

“…9 Therefore, widespread attentions of researchers has been devoted to the finite time performance of the systems, including finite-time control 10 and finite-time state estimation problems. 11,12 For instance, the finite-time event-driven control problem was addressed in Reference 10 for networked switched system with cyber-attacks. The event-triggered filter design was concerned in Reference 11 for networked systems suffering deception attacks.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Finite‐time adaptive event‐triggered asynchronous state estimation for Markov jump systems with cyber‐attacks

Zha

Liao

Liu

et al. 2021

Intl J Robust & Nonlinear

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This article considers the issue of finite‐time asynchronous state estimation for event‐triggered nonlinear Markovian jump systems subject to cyber‐attacks. An adaptive event‐triggered scheme is introduced to cope with the capacity constraint of the networked resources. It is assumed that the transmitted sensor measurements may experience randomly malicious cyber‐attacks. Considering the effect of the adaptive event‐triggered scheme and the occurrence of cyber‐attacks, we establish a new state estimation error system model. Sufficient conditions of the finite‐time boundedness and the finite‐time boundedness are developed for the augmented system. Then, the design methods of the asynchronous estimator gains are derived, which can ensure the finite‐time boundedness of the estimation error system. Finally, a numerical example is given to show the effectiveness of the theoretical results.

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Section: Resultsmentioning

confidence: 99%

Section: Numerical Examplesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Finite‐time adaptive event‐triggered asynchronous state estimation for Markov jump systems with cyber‐attacks

Zha

Liao

Liu

et al. 2021

Intl J Robust & Nonlinear

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“…There are many forms of cyber‐attacks, and they fall into two main categories: denial of service (DoS) attacks and deception attacks (including replay and false data injection attacks). The DoS attacks cause data loss by jamming wireless communication channels 9‐11 and the deception attacks interfere with network communication by injecting duplicate or spurious data, 12‐15 both of which deteriorate systems' security.…”

Section: Introductionmentioning

confidence: 99%

Optimal filter design for cyber‐physical systems under stealthy hybrid attacks

Lin

Lam

2020

Intl J Robust & Nonlinear

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This article studies the optimal filtering problem for cyber‐physical systems under stealthy hybrid cyber‐attacks. Due to the stealthiness of cyber‐attacks, the optimal filter (OF) consists of an exponentially increasing number of terms, which makes it computationally infeasible in practice. To address this computational issue, an optimal linear filter (OLF) is developed with its estimation performance fairly close to the optimal one. Furthermore, for a system under the stealthy hybrid cyber‐attacks, it is proved that when the system is stable, the exponentially increasing number of terms remain bounded, making the OF stable; and that the OLF is stable if and only if the system is stable. Finally, numerical examples are employed to illustrate the effectiveness of the proposed two filters.

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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%

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 finite-time H∞ filtering for networked systems under deception attacks

Cited by 51 publications

References 33 publications

Finite‐time adaptive event‐triggered asynchronous state estimation for Markov jump systems with cyber‐attacks

Finite‐time adaptive event‐triggered asynchronous state estimation for Markov jump systems with cyber‐attacks

Optimal filter design for cyber‐physical systems under stealthy hybrid attacks

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

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Event-triggered finite-time H∞ filtering for networked systems under deception attacks

Cited by 51 publications

References 33 publications

Finite‐time adaptive event‐triggered asynchronous state estimation for Markov jump systems with cyber‐attacks

Finite‐time adaptive event‐triggered asynchronous state estimation for Markov jump systems with cyber‐attacks

Optimal filter design for cyber‐physical systems under stealthy hybrid attacks

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

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Product

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