Adaptive event-triggered state estimation for complex networks with nonlinearities against hybrid attacks

Deng, Yahan; Meng, Zhenhai; Lu, Hongqian

doi:10.3934/math.2022158

Cited by 3 publications

(12 citation statements)

References 31 publications

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“…The cumulative error J 3 2 (k) IDB attack Bernoulli distributed DoS attack in [32] Uniform DoS attack in [24] Attack free F I G U R E 7 Cumulative error J 3 (k) under different attack models and without attack of node 3. Estimation of z 1 (k) in [42] Estimation of z 1 (k) in [43] F I G U R E 11 The system output z 1 (k) and its estimation under different estimation methods of node 1. Estimation of z 2 (k) in [42] Estimation of z 2 (k) in [43] F I G U R E 12 The system output z 2 (k) and its estimation under different estimation methods of node 2.…”

Section: Illustrative Examplesmentioning

confidence: 99%

“…Estimation of z 1 (k) in [42] Estimation of z 1 (k) in [43] F I G U R E 11 The system output z 1 (k) and its estimation under different estimation methods of node 1. Estimation of z 2 (k) in [42] Estimation of z 2 (k) in [43] F I G U R E 12 The system output z 2 (k) and its estimation under different estimation methods of node 2.…”

Section: Illustrative Examplesmentioning

confidence: 99%

“…The number of the attack strategy is 10 in three channels during the time horizon [0, 30] and the parameter 𝜌 i is chosen as 𝜌 1 = 2.75, 𝜌 2 = 2.84, 𝜌 3 = 2.87. Setting 𝜅 1 = 𝜅 2 = 𝜅 3 = 0.04 and using Algorithm 1, the parameter 𝜌 i is obtained as Estimation of z 3 (k) in [42] Estimation of z 3 (k) in [43] F I G U R E 13 The system output z 3 (k) and its estimation under different estimation methods of node 3. Estimation method in Theorem 2 Estimation method in [42] Estimation method in [43] F I G U R E 14 Cumulative error J 1 (k) of node 1.…”

Section: Illustrative Examplesmentioning

confidence: 99%

“…Setting 𝜅 1 = 𝜅 2 = 𝜅 3 = 0.04 and using Algorithm 1, the parameter 𝜌 i is obtained as Estimation of z 3 (k) in [42] Estimation of z 3 (k) in [43] F I G U R E 13 The system output z 3 (k) and its estimation under different estimation methods of node 3. Estimation method in Theorem 2 Estimation method in [42] Estimation method in [43] F I G U R E 14 Cumulative error J 1 (k) of node 1. Figures 11-13 display the system output z i (k) and the estimation ẑi (k) by using the different estimation methods: (1) The estimation method in Reference 42 for the system without attack.…”

Section: Illustrative Examplesmentioning

confidence: 99%

“…That is, the estimation accuracy of the nonlinear CNs (1) is best under the proposed resilient estimator in Theorem 2. Estimation method in Theorem 2 Estimation method in [42] Estimation method in [43] F I G U R E 15 Cumulative error J 2 (k) of node 2. Estimation method in Theorem 2 Estimation method in [42] Estimation method in [43] F I G U R E 16 Cumulative error J 3 (k) of node 3.…”

Section: Illustrative Examplesmentioning

confidence: 99%

See 4 more Smart Citations

Novel attack‐defense framework for nonlinear complex networks: An important‐data‐based method

Wang

Wei

Liu

2022

Intl J Robust & Nonlinear

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This article addresses the state estimation problem for a class of nonlinear complex networks (CNs) under attack. First, a novel important-data-based (IDB) attack strategy is skillfully proposed from the adversary's point of view to maximize the attack effect. Different from most existing attack models, the IDB attacker has the ability to eavesdrop measurements and only attacks the packets which play an important role in the system. As such, a larger system performance degradation can be expected. Second, a new kind of resilient H ∞ estimator is designed, from the perspective of the defenders, to alleviate the negative effect of the attack. In a word, a novel unified attack-defense framework for nonlinear CNs is established. In order to make up for the defect that the IDB attacker's parameter is unknown to the defender, an algorithm is developed to approximate the attack parameter. With the help of the Lyapunov functional method, sufficient conditions are obtained to resist the proposed IDB attack and ensure the H ∞ performance of the augmented system. At last, two examples are given to demonstrate the destructiveness of the proposed IDB attack strategy and the effectiveness of the developed resilient H ∞ estimator, respectively.

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Section: Illustrative Examplesmentioning

confidence: 99%

Section: Illustrative Examplesmentioning

confidence: 99%

Section: Illustrative Examplesmentioning

confidence: 99%

Section: Illustrative Examplesmentioning

confidence: 99%

Section: Illustrative Examplesmentioning

confidence: 99%

See 3 more Smart Citations

Novel attack‐defense framework for nonlinear complex networks: An important‐data‐based method

Wang

Wei

Liu

2022

Intl J Robust & Nonlinear

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Partial‐nodes‐based state estimation for state‐saturated complex networks with random mixed couplings under dynamic event‐triggered protocol

Lin

Chen

et al. 2023

Adaptive Control & Signal

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The state estimation (SE) problem is addressed for state-saturated complex networks (CNs) contaminated by the random mixed couplings. Such coupling phenomenon consists of the random inner coupling and the random outer coupling, where the random inner coupling is reflected by multiplicative noise and the random outer coupling is described by random variable obeying uniform distribution. In order to reduce resource waste and calculation cost, the additional auxiliary variable method is employed to construct the dynamic event-triggered communication condition, which can more effectively represent the change of information demand. Accordingly, a novel partial-nodes-based (PNB) estimator is designed in the presence of general nonlinearity and state-saturated nonlinearity, which can estimate the whole states of network nodes with the aid of the measurement information from partial nodes. The appropriate estimator gain is obtained to ensure that the upper bound of estimation error covariance (UBEEC) is minimized. Moreover, sufficient condition is given to guarantee that the trace of the UBEEC is uniformly bounded. Finally, the numerical example is presented to demonstrate the feasibility and effectiveness of the developed PNB estimation algorithm.

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Extended dissipative performance for a complex dynamical networks based on adaptive event‐triggered filter method subject to hybrid attacks

Tajudeen,

Ali,

Rajchakit

et al. 2023

Adaptive Control & Signal

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SummaryThe challenge of adaptive event‐triggered based filter design for complex dynamical networks with time‐varying coupling delays under hybrid attack is studied in this research. A new model of complex dynamical networks with input constraints, involving denial‐of‐service (DoS) attack, replay attack, and deception attack is designed. An adaptive event‐triggered scheme is applied to filter design, which adaptively modifies the communication threshold level in order to reduce the effect of the transmission load. By using Lyapunov theory and inequality techniques, a novel necessary condition is proposed that guarantees mean‐square asymptotically stability with extended dissipative performance. The proposed filter parameters are then given an explicit expression, which is solved using a set of linear matrix inequalities. Finally, two numerical results are presented to demonstrate the validity of the theoretical result suggested.

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Adaptive event-triggered state estimation for complex networks with nonlinearities against hybrid attacks

Cited by 3 publications

References 31 publications

Novel attack‐defense framework for nonlinear complex networks: An important‐data‐based method

Novel attack‐defense framework for nonlinear complex networks: An important‐data‐based method

Partial‐nodes‐based state estimation for state‐saturated complex networks with random mixed couplings under dynamic event‐triggered protocol

Extended dissipative performance for a complex dynamical networks based on adaptive event‐triggered filter method subject to hybrid attacks

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