Event-Triggered State Observers for Sparse Sensor Noise/Attacks

Shoukry, Yasser; Tabuada, Paulo

doi:10.1109/tac.2015.2492159

Cited by 356 publications

(259 citation statements)

References 24 publications

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“…Definition 1 ( [19]): An LTI system from (1) is said to be s-sparse observable if for every set K ⊂ S of size s (i.e., |K| = s), the pair (A, P K C) is observable.…”

Section: Attack-resilient State Estimatorsmentioning

confidence: 99%

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Attack-resilient state estimation in the presence of noise

Pajić

Tabuada

Lee

et al. 2015

2015 54th IEEE Conference on Decision and Control (CDC)

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Abstract-We consider the problem of attack-resilient state estimation in the presence of noise. We focus on the most general model for sensor attacks where any signal can be injected via the compromised sensors. An l0-based state estimator that can be formulated as a mixed-integer linear program and its convex relaxation based on the l1 norm are presented. For both l0 and l1-based state estimators, we derive rigorous analytic bounds on the state-estimation errors. We show that the worst-case error is linear with the size of the noise, meaning that the attacker cannot exploit noise and modeling errors to introduce unbounded state-estimation errors. Finally, we show how the presented attack-resilient state estimators can be used for sound attack detection and identification, and provide conditions on the size of attack vectors that will ensure correct identification of compromised sensors.

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“…Definition 1 ( [19]): An LTI system from (1) is said to be s-sparse observable if for every set K ⊂ S of size s (i.e., |K| = s), the pair (A, P K C) is observable.…”

Section: Attack-resilient State Estimatorsmentioning

confidence: 99%

“…Finally, if we consider systems that can deal with up to q attacks when there is no noise, from (19) and (20) it follows that the feasible set for the state estimation vector ∆x l1 can be described as the set where ∆x l1 = 0 or it satisfies…”

Section: Attack-resilient State Estimatorsmentioning

confidence: 99%

Attack-resilient state estimation in the presence of noise

Pajić

Tabuada

Lee

et al. 2015

2015 54th IEEE Conference on Decision and Control (CDC)

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“…The particular assumption is that the pair (A, C) remains observable after removing any set of 2 |K| rows of C (that is, any set of 2 |K| sensors) [12,Propositions 3.2 and 3.3]. If this strong observability assumption is not met, or in case of state attacks on (regular or singular) systems, the problem remains computationally hard.…”

Section: Lemma 22 (Complexity Of the Attack Identification Problem)mentioning

confidence: 99%

“…Convex relaxation techniques are employed in [11] to derive an efficient (yet incomplete and without guarantees) identification algorithm for the case of attacks against the system measurements. Finally, [12] shows that certain instances of the identification problem can in fact be solved efficiently. In this work we derive decentralized and distributed identification monitors with performance guarantees.…”

Section: Introductionmentioning

confidence: 99%

A divide-and-conquer approach to distributed attack identification

Pasqualetti

Dörfler

Bullo

2015

2015 54th IEEE Conference on Decision and Control (CDC)

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Identifying attacks is key to ensure security in cyber-physical systems. In this note we remark upon the computational complexity of the attack identification problem by showing how conventional approximation techniques may fail to identify attacks. Then, we propose decentralized and distributed monitors for attack identification with performance guarantees and low computational complexity. The proposed monitors rely on the geometric framework proposed in [1], yet require only local knowledge of the system dynamics and parameters. We exploit a divide-and-conquer approach, where first the system is partitioned into disjoint regions, then corrupted regions are identified via distributed computation, and finally corrupted components are isolated within regions.

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“…Here, we assume that control design for no-attack case has been developed and concentrate on techniques for state estimation and sensor fusion under external attacks. Note that since previously developed methods for attackresilient state estimation (e.g., [15,39,48]) require the unpractical assumption of the exact knowledge of the controlled plant's dynamics, resilience-to-attack guarantees do not hold when these assumptis are violated.…”

Section: Platform-aware Control Design For Secure Cpsmentioning

confidence: 99%

Design methodologies for securing cyber-physical systems

Faruque¹,

Regazzoni²,

Pajić³

2015

2015 International Conference on Hardware/Software Codesign and System Synthesis (CODES+ISSS)

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Cyber-Physical Systems (CPS) are in most cases safety-and mission-critical. Standard design techniques used for securing embedded systems are not suitable for CPS due to the restricted computation and communication budget available in the latter. In addition, the sensitivity of sensed data and the presence of actuation components further increase the security requirements of CPS. To address these issues, it is necessary to provide new design methods in which security is considered from the beginning of the whole design flow and addressed in a holistic way. In this paper, we focus on the design of secure CPS as part of the complete CPS design process, and provide insights into new requirements on platform-aware design of control components, design methodologies and architectures posed by CPS design. We start by discussing methods for the multi-disciplinary modeling, simulation, tools, and software synthesis challenges for CPS. We also present a framework for design of secure control systems for CPS, while taking into account properties of the underlying computation and communication platforms. Finally, we describe the security challenges in the computing hardware that is used in CPS.

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Event-Triggered State Observers for Sparse Sensor Noise/Attacks

Cited by 356 publications

References 24 publications

Attack-resilient state estimation in the presence of noise

Attack-resilient state estimation in the presence of noise

A divide-and-conquer approach to distributed attack identification

Design methodologies for securing cyber-physical systems

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