Security in stochastic control systems: Fundamental limitations and performance bounds

Bai, Cheng-Zong; Pasqualetti, Fabio; Gupta, Vijay

doi:10.1109/acc.2015.7170734

Cited by 101 publications

(77 citation statements)

References 12 publications

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“…Manandhar et al [11] studied the false data injection attack on the state-space model of a power system, and the attackers were also assumed to have the prior knowledge of the system model. Similar assumptions can be found in the recent work of Smith [12], Bai et al [13], Teixeira et al [14] and Pang et al [15].…”

Section: Introductionsupporting

confidence: 67%

Greedy Modes of Data Integrity Attacks on Industrial Control Systems: A Case Study of Tennessee Eastman Process

Li¹,

Xie²,

Yu³

et al. 2017

IJSIA

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Section: Introductionsupporting

confidence: 67%

Greedy Modes of Data Integrity Attacks on Industrial Control Systems: A Case Study of Tennessee Eastman Process

Li¹,

Xie²,

Yu³

et al. 2017

IJSIA

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“…converges to the false alarm rate as δ → 0. Moreover, Bai et al [29] have proven a similar result for other forms of detectors.…”

Section: Classifications For Systems and Attacksmentioning

confidence: 61%

“…Proof. We only need to prove that the system (29)-(30) is not invertible if and only if (33) is not invertible: Suppose that the input ζ k for system (29)…”

Section: Proof See Appendix Amentioning

confidence: 99%

The Vulnerability of Cyber-Physical System Under Stealthy Attacks

Sui

Marelli

et al. 2021

IEEE Trans. Automat. Contr.

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In this paper, we study the impact of stealthy attacks on the Cyber-Physical System (CPS) modeled as a stochastic linear system. An attack is characterised by a malicious injection into the system through input, output or both, and it is called stealthy (resp. strictly stealthy) if it produces bounded changes (resp. no changes) in the detection residue. Correspondingly, a CPS is called vulnerable (resp. strictly vulnerable) if it can be destabilized by a stealthy attack (resp. strictly stealthy attack). We provide necessary and sufficient conditions for the vulnerability and strictly vulnerability. For the invulnerable case, we also provide a performance bound for the difference between healthy and attacked system. Numerical examples are provided to illustrate the theoretical results.

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“…There has recently been significant interest and work in the broad area of security of cyber-physical systems (CPS), see for example [1]- [8]. This topic investigates the properties of conventional control systems in the presence of adversarial disturbances.…”

Section: Introductionmentioning

confidence: 99%

On Reachable Sets of Hidden CPS Sensor Attacks

Murguia

Ruths

2018

2018 Annual American Control Conference (ACC)

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For given system dynamics, observer structure, and observer-based fault/attack detection procedure, we provide mathematical tools -in terms of Linear Matrix Inequalities (LMIs) -for computing outer ellipsoidal bounds on the set of estimation errors that attacks can induce while maintaining the alarm rate of the detector equal to its attack-free false alarm rate. We refer to these sets to as hidden reachable sets. The obtained ellipsoidal bounds on hidden reachable sets quantify the attacker's potential impact when it is constrained to stay hidden from the detector. We provide tools for minimizing the volume of these ellipsoidal bounds (minimizing thus the reachable sets) by redesigning the observer gains. Simulation results are presented to illustrate the performance of our tools.

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Security in stochastic control systems: Fundamental limitations and performance bounds

Cited by 101 publications

References 12 publications

Greedy Modes of Data Integrity Attacks on Industrial Control Systems: A Case Study of Tennessee Eastman Process

Greedy Modes of Data Integrity Attacks on Industrial Control Systems: A Case Study of Tennessee Eastman Process

The Vulnerability of Cyber-Physical System Under Stealthy Attacks

On Reachable Sets of Hidden CPS Sensor Attacks

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