A resilient framework for sensor-based attacks on cyber–physical systems using trust-based consensus and self-triggered control

Croteau, Brien; Rodriguez-Seda, Erick J.; Kiriakidis, Kiriakos; Robucci, Ryan; Patel, Chintan

doi:10.1016/j.conengprac.2020.104509

Cited by 15 publications

(6 citation statements)

References 34 publications

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“…Pre and postmultiplying (19), respectively, by [x k w k ] and its transpose, and considering (18), it follows that:…”

Section: Design Of the H ∞ State-feedback Controllermentioning

confidence: 99%

“…In particular, this article focuses on secure control against deception attacks, which, while attracting growing interest, is still in an early stage. On this subject, most approaches for mitigating deception attacks are based on the assumption that some sensors/actuators are not compromised (sparse attacks) [14]- [16], in exploiting the analytical redundancy obtained in the context of distributed systems [4], [7], [17], [18] or in the use of stochastic models with known distribution parameters for modeling the attacks [19]- [22]. Whereas the above approaches are efficient under the proposed assumptions, the real attacker behavior may be incompatible with those attack models and/or the safe redundancy may be unavailable.…”

mentioning

confidence: 99%

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Dual-Rate Control Framework With Safe Watermarking Against Deception Attacks

Bessa

Trapiello

Puig

et al. 2022

IEEE Trans. Syst. Man Cybern, Syst.

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This article presents a novel secure-control framework against sensor deception attacks. The vulnerability of cyber-physical systems with respect to sensor deceptive attacks makes that all sensor measurements are not reliable until the system security is assured by an attack detection module. Most of the active attack detection strategies require some time to assess the system security, while injecting a watermark signal to ease the detection. However, the injection of watermark signals deteriorates the performance and stability of the plant. The proposed control framework consists of a dual-rate control (DRC) that is able to stabilize the plant using: 1) a model predictive controller that operates at a slower sampling time; 2) a state-feedback predictor-based controller that operates in the nominal sampling time disregarding the use of the untrustworthy measurements until the attack detector is able to certify the security; and 3) a reconfiguration block (RB) for palliating the effect of the watermarking. Simulation results indicate the efficacy of the proposed DRC framework to defend the system from cyber-attacks and the ability of the RB to improve the closed-loop performance during the watermark injection.

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“…Pre and postmultiplying (19), respectively, by [x k w k ] and its transpose, and considering (18), it follows that:…”

Section: Design Of the H ∞ State-feedback Controllermentioning

confidence: 99%

mentioning

confidence: 99%

Dual-Rate Control Framework With Safe Watermarking Against Deception Attacks

Bessa

Trapiello

Puig

et al. 2022

IEEE Trans. Syst. Man Cybern, Syst.

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show abstract

“…[130], [131], [132], [133], [134], [135], [136] Node MTD ✓ ✓ ✓ ✓ ✓ [137], [149], [150], [141], [132], [133], [146], [150], [145], [146] [147], [148] Dynamic Software Evolution ✓ ✓ ✓ [151], [87], [152], [153] Consensus & Distributed Trust ✓ ✓ ✓ ✓ [160], [161], [162], [163], [164],…”

Section: Risk Management Vs Cyber-resiliencementioning

confidence: 99%

“…In this case, at each update, the controller ignores suspicious values and computes the control input with the non-suspicious values. For example, using Distributed Kalman Filter for resilient state estimation [162,163] or other distributed observers strategies to manage sensor compromise [161,186]. Other strategies are distributed function calculation in the presence of malicious agents [160], distributed multi-agent consensus [154,155,164,187,188], resilient vector consensus [156,158] and resilient leader-followers consensus approaches [157,159].…”

Section: 34mentioning

confidence: 99%

Cyber-Resilience Evaluation of Cyber-Physical Systems

Segovia

Rubio‐Hernan

Cavalli

et al. 2020

2020 IEEE 19th International Symposium on Network Computing and Applications (NCA)

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Concerns for the resilience of Cyber-Physical Systems (CPS) in critical infrastructure are growing. CPS integrate sensing, computation, control and networking into physical objects and mission-critical services, connecting traditional infrastructure to internet technologies.While this integration increases service efficiency, it has to face the possibility of new threats posed by the new functionalities. This leads to cyber-threats, such as denial-of-service, modification of data, information leakage, spreading of malware, and many others.Cyber-resilience refers to the ability of a CPS to prepare, absorb, recover, and adapt to the adverse effects associated with cyber-threats, e.g., physical degradation of the CPS performance resulting from a cyber-attack. Cyber-resilience aims at ensuring CPS survival, by keeping the core functionalities of the CPS in case of extreme events. The literature on cyber-resilience is rapidly increasing, leading to a broad variety of research works addressing this new topic. In this article, we create a systematization of knowledge about existing scientific efforts of making CPS cyber-resilient. We systematically survey recent literature addressing cyber-resilience with a focus on techniques that may be used on CPS. We first provide preliminaries and background on CPS and threats, and subsequently survey state-of-the-art approaches that have been proposed by recent research work applicable to CPS. In particular, we aim at differentiating research work from traditional risk management approaches, based on the general acceptance that it is unfeasible to prevent and mitigate all possible risks threatening a CPS. We also discuss questions and research challenges, with a focus on the practical aspects of cyber-resilience, such as the use of metrics and evaluation methods, as well as testing and validation environments.

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“…In [9], a resilient control strategy is proposed to realize the desired trajectory tracking of CPSs under false data injection attacks. In [10], the author proposes an attack-tolerance control combined with a self-triggering mechanism that can perceive communication, which increases the data fidelity of sensors in CPSs. For the linear cyber-physical systems under physical fault and false data injection attacks, paper [11] introduces an event trigger mechanism and proposes an observer-based output feedback control strategy.…”

Section: Introductionmentioning

confidence: 99%

Adaptive neural network control for nonlinear cyber-physical systems subject to false data injection attacks with prescribed performance

Liu

Tang

Zhao

et al. 2021

Phil. Trans. R. Soc. A.

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Cyber-physical systems (CPSs), as emerging products of industry 4.0 , play a key role in the development of intelligent manufacturing. This paper proposes an observer-based adaptive neural network (NN) control for nonlinear strict-feedback CPSs subject to false data injection attacks. Since there may be strict constraints on the state or output signals of nonlinear cyber-physical systems (NCPSs), we propose a time-varying asymmetric barrier Lyapunov function to realize the specific output constraints of NCPSs under cyber-attacks. Besides, since false data injection attacks will corrupt the transmitted state variables, an observer is designed to obtain observations of the exact states, and NN is used to approximate the unknown nonlinearity of NCPSs. With the proposed control strategy, the constraint control problem of NCPSs subject to false data injection attacks is settled. Finally, a numerical simulation example verifies the effectiveness of the proposed controller. This article is part of the theme issue ‘Towards symbiotic autonomous systems’.

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A resilient framework for sensor-based attacks on cyber–physical systems using trust-based consensus and self-triggered control

Cited by 15 publications

References 34 publications

Dual-Rate Control Framework With Safe Watermarking Against Deception Attacks

Dual-Rate Control Framework With Safe Watermarking Against Deception Attacks

Cyber-Resilience Evaluation of Cyber-Physical Systems

Adaptive neural network control for nonlinear cyber-physical systems subject to false data injection attacks with prescribed performance

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