Robust Resilient Signal Reconstruction under Adversarial Attacks

Zheng, Yu; Anubi, Olugbenga Moses; Mestha, Lalit K.; Achanta, Hema Kumari

doi:10.48550/arxiv.1807.08004

Cited by 4 publications

(11 citation statements)

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“…Failure of CPS is more complicated than random failures or well-defined uncertainty for which many results exist on reliability and robustness, since they may be caused by stealth malicious attacks. One of such powerful deception attacks, named false data injection attack (FDIA), has shown ability to bypass bad data detector (BDD), while compromising the integrity of observer and robust controller with sparse measurements corruption [4,5]. Consequently, much research attention have been directed to develop appropriate protection schemes.…”

Section: Introductionmentioning

confidence: 99%

“…Due to sparsity assumption of the attack vector |supp(e)| ≤ k, the resilient estimation problem has been cast as a classical error correction problem [5,11]. Consider a linear observation model y = Hx + e, where H ∈ R N×n denotes an observation matrix, then the resilient estimation is formulated as 0-norm minimization problem [12].…”

Section: Introductionmentioning

confidence: 99%

“…In order to guarantee correct state recoveries for larger percentage of attacks, prior information has been considered for resilient estimation scheme in literature: Measurement Prior [7,15], Support Prior [5] and State Prior [16]. In [16], the author considered the Prior information of estimated states in three forms: sparsity information of x 0 , (α, n 0 ) sparsity information where α replaces 0 in sparsity definition, and side information that is knowledge of the initial state from the physical attribution of the system and cannot be manipulated by malicious third parties.…”

Section: Introductionmentioning

confidence: 99%

“…Then a weighted 1-norm minimization scheme [17] is given based on the resulting pruned support prior. The pruning idea originates from [5]. Moreover, due to the perfect localization precision of pruned support prior, resilient Unscented Kalman Filter (UKF) against FDIA was given in [18] by performing UKF based on the pruned safe set.…”

Section: Introductionmentioning

confidence: 99%

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Attack-Resilient Weighted $\ell_1$ Observer with Prior Pruning

Anubi

2021

Preprint

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Security related questions for Cyber Physical Systems (CPS) have attracted much research attention in searching for novel methods for attack-resilient control and/or estimation. Specifically, false data injection attacks (FDIAs) have been shown to be capable of bypassing bad data detection (BDD), while arbitrarily compromising the integrity of state estimators and robust controller even with very sparse measurements corruption. Moreover, based on the inherent sparsity of pragmatic attack signals, 1 -minimization scheme has been used extensively to improve the design of attackresilient estimators. For this, the theoretical maximum for the percentage of compromised nodes that can be accommodated has been shown to be 50%. In order to guarantee correct state recoveries for larger percentage of attacked nodes, researchers have begun to incorporate prior information into the underlying resilient observer design framework. For the most pragmatic cases, this prior information is often obtained through some data-driven machine learning process. Existing results have shown strong positive correlation between the tolerated attack percentages and the precision of the prior information. In this paper, we present a pruning method to improve the precision of the prior information, given corresponding stochastic uncertainty characteristics of the underlying machine learning model. Then a weighted 1 -minimization is proposed based on the pruned prior. The theoretical and simulation results show that the pruning method significantly improves the observer performance for much larger attack percentages, even when moderately accurate machine learning model used.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Attack-Resilient Weighted $\ell_1$ Observer with Prior Pruning

Anubi

2021

Preprint

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“…In this paper, we build on our previous works on enhancing the recoverability of resilient estimators by incorporating prior information, either in form of attack-support estimation [23] or through a more general set inclusion constraint [24]. Here, we provide theoretical guarantees of how certain boundedness property of the prior information set can improve the reconstruction error bound of the resulting resilient estimator.…”

Section: Introductionmentioning

confidence: 99%

Resilient Optimal Estimation Using Measurement Prior

Anubi,

Konstantinou,

Roberts

2019

Preprint

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This paper considers the problem of optimal estimation for linear system with the measurement vector subject to arbitrary corruption by an adversarial agent. This problem is relevant to cyber-physical systems where, due to the tight coupling of physics, communication and computation, a malicious agent is able to exploit multiple inherent vulnerabilities in order to inject stealthy signals into the measurement process. These malicious signals are calculated to serve the attack objectives of causing false situation awareness and/or triggering a sequence of cascading effects leading to an ultimate system failure. We assume that the attacker can only compromise a portion, but not all, of the measurement channels simultaneously. However, once a channel is compromised, the attacker is free to modify the corresponding measurement arbitrarily.Consequently, the problem is formulated as a compressive sensing problem with additional prior-information model. The prior-information considered is a set inclusion constraint on the measurement vector. It is shown that if the prior set satisfies certain conditions, the resulting recovery error bound is much stronger. The approach is applied to the problem of resilient sate estimation of a power system. For this application, Gaussian Process is used to build a prior generative probabilistic regression model from historical data. The resulting Gaussian Process Regression model recursively maps energy market information to iid Gaussian distributions on the relevant system measurements. An optimization-based resilient state estimator is then developed using a re-weighted 1-minimization scheme. The developed algorithm is evaluated through a numerical simulation example of the IEEE 14-bus system mapped to the New York Independent System Operator (NYISO) grid data.

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Multi-Model Resilient Observer under False Data Injection Attacks

Anubi¹,

Konstantinou²,

Wong³

et al. 2020

Preprint

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In this paper, we present the concept of boosting the resiliency of optimization-based observers for cyber-physical systems (CPS) using auxiliary sources of information. Due to the tight coupling of physics, communication and computation, a malicious agent can exploit multiple inherent vulnerabilities in order to inject stealthy signals into the measurement process. The problem setting considers the scenario in which an attacker strategically corrupts portions of the data in order to force wrong state estimates which could have catastrophic consequences. The goal of the proposed observer is to compute the true states inspite of the adversarial corruption. In the formulation, we use a measurement prior distribution generated by the auxiliary model to refine the feasible region of a traditional compressive sensingbased regression problem. A constrained optimization-based observer is developed using l1-minimization scheme. Numerical experiments show that the solution of the resulting problem recovers the true states of the system. The developed algorithm is evaluated through a numerical simulation example of the IEEE 14-bus system.

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Robust Resilient Signal Reconstruction under Adversarial Attacks

Cited by 4 publications

References 0 publications

Attack-Resilient Weighted $\ell_1$ Observer with Prior Pruning

Attack-Resilient Weighted $\ell_1$ Observer with Prior Pruning

Resilient Optimal Estimation Using Measurement Prior

Multi-Model Resilient Observer under False Data Injection Attacks

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