Quantized output feedback control for switched systems with DoS attacks and event-triggered sampling

Yan, Jianguo; Shi, Lixiang; Xia, Yuanqing; Zhang, Yingqi

doi:10.1016/j.jfranklin.2022.09.005

Cited by 9 publications

(3 citation statements)

References 30 publications

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“…Uniform quantization plays a critical role in data transmission by reducing the volume of signal data requiring transmission, thereby adapting to network bandwidth constraints and significantly enhancing transmission efficiency [30][31][32][33]. Moreover, the deliberate design enables quantization to bolster resilience against DoS attacks by facilitating signal reconstruction and maintaining stability during transmission [34][35][36].…”

Section: Secure Quantization Under Dos Attacksmentioning

confidence: 99%

Resilient Integrated Control for AIOT Systems under DoS Attacks and Packet Loss

Cao,

Wang,

Chen

et al. 2024

Electronics

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This paper addresses bandwidth limitations resulting from Denial-of-Service (DoS) attacks on Artificial Intelligence of Things (AIOT) systems, with a specific focus on adverse network conditions. First, to mitigate the impact of DoS attacks on system bandwidth, a novel model predictive control combined with a dynamic time-varying quantization interval adjustment technique is designed for the encoder–decoder architecture of AIOT systems. Second, the network state is modeled to represent a Markov chain under suboptimal network conditions. Furthermore, to guarantee the stability of AIOT systems under random packet loss, a Kalman filter algorithm is applied to precisely estimate the system state. By leveraging the Lyapunov stability theory, the maximum tolerable probability of random packet loss is determined, thereby enhancing the system’s resilient operation. Simulation results validate the effectiveness of the proposed method in dealing with DoS attacks and adverse network conditions.

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Section: Secure Quantization Under Dos Attacksmentioning

confidence: 99%

Resilient Integrated Control for AIOT Systems under DoS Attacks and Packet Loss

Cao,

Wang,

Chen

et al. 2024

Electronics

View full text Add to dashboard Cite

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“…Assumption 1 (attack duration) [42]: Let Ξ(0, k) denote the total number that the system under the DoS attacks in interval [0, k], there exist the duration bound of attacks ϕ d ∈ [0, 1], ϕ 0 ≥ 0, and…”

Section: System Formulationmentioning

confidence: 99%

“…Assumption 2 (attack frequency) [42]: Let N d (0, k) represent the launch number of DoS attacks during the interval [0, k], there exist the attack frequency bound λ d ∈ [0, 1 2 ], λ 0 ≥ 0, and k ∈ N + , such that…”

Section: System Formulationmentioning

confidence: 99%

Event-triggered Optimal Consensus for Discrete-time Nonlinear Multiagent Systems with DoS attacks via Reinforcement Learning Method

Liao,

Wang,

Wang

et al. 2024

Preprint

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This paper focuses on the event-triggered optimal control problem of discrete-time nonlinear multiagent systems (MASs) via the reinforcement learning method. Distinguished from the existing consensus protocols for discrete-time multiagent systems under the ideal communication scenario, the considered model suffers from input saturation, unknown external disturbance, and denial-of-service (DoS) attacks. According to the approximation capability of the radial basis function neural network(RBF NN), the disturbance network is established to tackle the effect of the unknown disturbance on consensus issues. Subsequently, the composite controller is derived with the reinforcement learning strategy to deal with the DoS attacks, where the update frequency of the actor-critic networks and controllers is determined by the novel event-triggered mechanism. Finally, two simulations are formulated to verify the feasibility of the proposed method.

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