Stabilization of Networked Control Systems Under DoS Attacks and Output Quantization

Wakaiki, Masashi; Cetinkaya, Ahmet; Ishii, Hideaki

doi:10.1109/tac.2019.2949096

Cited by 78 publications

(68 citation statements)

References 78 publications

(177 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These notions have been popularly used in the literature; see e.g., [13], [14], [17], [18], [38]. When studying discrete-time systems, a discrete-time DoS model that is similar to the present one, was discussed in [15]. In their work, LTI systems without noise were considered, and thus weaker assumptions were used.…”

Section: B Denial-of-service Attackmentioning

confidence: 99%

“…In this section, we address the challenging stabilization problem of unknown LTI systems in the presence of DoS attacks. Our proposal is to redesign the robust predictor-based controller in (17) leveraging the Fundamental Lemma (15), to develop a novel resilient control scheme that capitalizes purely on data. Specifically, a data-dependent controller is first constructed to follow the predict-then-reset circle, which resembles the data-driven MPC developed in [32].…”

Section: Data-driven Resilient Control Under Dosmentioning

confidence: 99%

See 1 more Smart Citation

Data-Driven Resilient Predictive Control under Denial-of-Service

Liu¹,

Sun²,

Wang³

et al. 2021

Preprint

View full text Add to dashboard Cite

The study of resilient control of linear time-invariant (LTI) systems against denial-of-service (DoS) attacks is gaining popularity in emerging cyber-physical applications. In previous works, explicit system models are required to design a predictorbased resilient controller. These models can be either given a priori or obtained through a prior system identification step. Recent research efforts have focused on data-driven control based on pre-collected input-output trajectories (i.e., without explicit system models). In this paper, we take an initial step toward datadriven stabilization of stochastic LTI systems under DoS attacks, and develop a resilient model predictive control (MPC) scheme driven purely by data-dependent conditions. The proposed datadriven control method achieves the same level of resilience as the model-based control method. For example, local input-to-state stability (ISS) is achieved under mild assumptions on the noise and the DoS attacks. To recover global ISS, two modifications are further suggested at the price of reduced resilience against DoS attacks or increased computational complexity. Finally, a numerical example is given to validate the effectiveness of the proposed control method.Index terms-Denial-of-service attack, data-driven control, model predictive control, input-to-state stability.

show abstract

Section: B Denial-of-service Attackmentioning

confidence: 99%

Section: Data-driven Resilient Control Under Dosmentioning

confidence: 99%

Data-Driven Resilient Predictive Control under Denial-of-Service

Liu¹,

Sun²,

Wang³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…A key question is how to select/how many should be the bits of the quantizer such that quantization error converges to zero eventually. There are several works which investigate stabilization of linear systems with quantized state feedback such as Wakaiki et al (2020); Ling (2017); Feng et al (2020). They show that the data rate to stabilize the systems under DoS attacks should be no less than that without DoS amplified by a term related to the frequency and duration of DoS.…”

Section: Introductionmentioning

confidence: 99%

Quantized state feedback stabilization of nonlinear systems under Denial-of-Service

2022

Self Cite

View full text Add to dashboard Cite

“…Some recent results in security control against cyberattacks have been summarized in Reference 11 and the authors in Reference 12 have discussed recursive filtering algorithms facing communication scheduling and network security. As stated in Reference 11, cyberattacks mainly contain deception attacks (data injection attacks), 13,14 replay attacks, 15 and DoS attacks [16][17][18] from defender's perspectives. Among them, DoS attacks are more frequently utilized to prevent data transmission, which may cause system performance degradation.…”

Section: Introductionmentioning

confidence: 99%

Memory‐based dynamic event‐triggered control for networked interval type‐2 fuzzy systems subject to DoS attacks

2021

Adaptive Control & Signal

View full text Add to dashboard Cite

In this article, the memory-based dynamic event-triggered controller design issue is investigated for networked interval type-2 (IT2) fuzzy systems under non-periodic denial-of-service (DoS) attacks. For saving limited network bandwidth, a novel memory-based dynamic event-triggered mechanism (DETM) is proposed to schedule data communication. Unlike existing event-triggered generators, the developed memory-based DETM can utilize a series of newly released signals and further save network resources by introducing interval dynamic variables. Moreover, to improve design flexibility, an IT2 fuzzy controller with freely selectable fuzzy rule number and premise membership functions (MFs) is synthesized. Then, a new switched time-delay system with imperfectly matched MFs is established under the consideration of memory-based DETM and DoS attacks simultaneously. Besides, based on the property of MFs, the boundary information of membership grades and slack matrices are introduced in the stability analysis. Furthermore, by using a piecewise Lyapunov-Krasovskii method, membership-functions-dependent criteria are deduced to ensure the asymptotic stability of built fuzzy switched systems. Finally, the effectiveness of proposed control strategies is demonstrated by simulation examples.

show abstract

Stabilization of Networked Control Systems Under DoS Attacks and Output Quantization

Cited by 78 publications

References 78 publications

Data-Driven Resilient Predictive Control under Denial-of-Service

Data-Driven Resilient Predictive Control under Denial-of-Service

Quantized state feedback stabilization of nonlinear systems under Denial-of-Service

Memory‐based dynamic event‐triggered control for networked interval type‐2 fuzzy systems subject to DoS attacks

Contact Info

Product

Resources

About