Detection of False Data Injection Attacks in Unobservable Power Systems by Laplacian Regularization

Dabush, Lital; Routtenberg, Tirza

doi:10.1109/sam53842.2022.9827810

Cited by 3 publications

(15 citation statements)

References 25 publications

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“… The Laplacian-Regularized detector (Lap.Reg.) from our previous work in [ 41 ], which also uses the GSP-WLS estimator,

. …”

Section: Resultsmentioning

confidence: 99%

“… Bad data detection: the ROC of the LNR test ( 34 ),

test ( 35 ), GFT−based detector [ 7 ] implemented with the GSP−WLS ( 26 ) and ( 27 ), and the pm−WLS [ 11 ] estimators,

[ 69 ], and the Laplacian−regularized [ 41 ] detectors with

buses. …”

Section: Figurementioning

confidence: 99%

“…GSP is an emerging field that extends concepts and techniques from traditional digital signal processing (DSP) to data on graphs [ 36 , 37 , 38 , 39 ]. Recent works have proposed the integration of GSP in power systems, such as the application of the GSP framework for the power grid with PMU data in [ 40 ], the spectral graph analysis of power flows in [ 35 ], and attack detection by GSP in [ 7 , 41 , 42 ]. For unobservable power systems, the authors of [ 43 ] investigate the use of GSP methods for dynamic state estimation in unobservable systems based on PMUs and advanced metering infrastructures (AMIs) under the assumption of bandlimited graph signals.…”

Section: Introductionmentioning

confidence: 99%

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State Estimation in Partially Observable Power Systems via Graph Signal Processing Tools

Dabush

Kroizer

Routtenberg

2023

Sensors

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This paper considers the problem of estimating the states in an unobservable power system, where the number of measurements is not sufficiently large for conventional state estimation. Existing methods are either based on pseudo-data that is inaccurate or depends on a large amount of data that is unavailable in current systems. This study proposes novel graph signal processing (GSP) methods to overcome the lack of information. To this end, first, the graph smoothness property of the states (i.e., voltages) is validated through empirical and theoretical analysis. Then, the regularized GSP weighted least squares (GSP-WLS) state estimator is developed by utilizing the state smoothness. In addition, a sensor placement strategy that aims to optimize the estimation performance of the GSP-WLS estimator is proposed. Simulation results on the IEEE 118-bus system show that the GSP methods reduce the estimation error magnitude by up to two orders of magnitude compared to existing methods, using only 70 sampled buses, and increase of up to 30% in the probability of bad data detection for the same probability of false alarms in unobservable systems The results conclude that the proposed methods enable an accurate state estimation, even when the system is unobservable, and significantly reduce the required measurement sensors.

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“… The Laplacian-Regularized detector (Lap.Reg.) from our previous work in [ 41 ], which also uses the GSP-WLS estimator,

. …”

Section: Resultsmentioning

confidence: 99%

“… Bad data detection: the ROC of the LNR test ( 34 ),

test ( 35 ), GFT−based detector [ 7 ] implemented with the GSP−WLS ( 26 ) and ( 27 ), and the pm−WLS [ 11 ] estimators,

[ 69 ], and the Laplacian−regularized [ 41 ] detectors with

buses. …”

Section: Figurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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State Estimation in Partially Observable Power Systems via Graph Signal Processing Tools

Dabush

Kroizer

Routtenberg

2023

Sensors

Self Cite

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“…the graph. The GSP-based detector in ( 16) was implemented in [39] with the ideal GHPF defined in (11), and in [50] with the graph TV filter from (10), both with L = B.…”

Section: Gsp-based Fdi Attack Detectionmentioning

confidence: 99%

“…GSP theory includes methods such as the Graph Fourier Transform (GFT), graph filters [44]- [46], and sampling and recovery of graph signals [47]- [49]. By leveraging the graphical properties of the states in PSSE, the works in [39], [40], [50], [51] present GSPbased detectors that are able to detect existing unobservable FDI attacks. The advantages of GSP-based detection methods include their ability to operate with limited data, lack of restrictive assumptions on the adversary's capabilities, and greater resilience to minor changes in the system's topology.…”

Section: Introductionmentioning

confidence: 99%

Structural-Constrained Methods for the Identification of False Data Injection Attacks in Power Systems

Morgenstern

Routtenberg

2022

IEEE Access

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Power system functionality is determined on the basis of power system state estimation (PSSE). Thus, corruption of the PSSE may lead to severe consequences, such as disruptions in electricity distribution, maintenance damage, and financial losses. Classical bad data detection (BDD) methods, developed to ensure PSSE reliability, are unable to detect well-designed attacks, named unobservable false data injection (FDI) attacks. In this paper, we develop novel structural-constrained methods for the detection of unobservable FDI attacks and the identification of the attacked buses. The proposed methods are based on formulating structural, sparse constraints on both the attack and the system loads. First, we exploit these constraints in order to compose an appropriate model selection problem. Then, we develop the associated generalized information criterion (GIC) for this problem. However, the GIC method's computational complexity grows exponentially with the network size, which may be prohibitive for large networks. Therefore, based on the proposed structural and sparse constraints, we develop two novel lowcomplexity methods for the practical identification of unobservable FDI attacks: 1) a modification of the state-of-the-art orthogonal matching pursuit (OMP) method; and 2) a method that utilizes the graph Markovian property in power systems, i.e. the second-neighbor relationship between the power data at the system's buses. In order to analyze the performance of the proposed methods, the appropriate oracle and clairvoyant detectors are also derived. The performance of the proposed methods is evaluated on the IEEE-30 bus test case. INDEX TERMSAttack detection and identification, false data injection (FDI) attacks, graph Markovian property, model selection, structural constraints,

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Invited Paper: Detection of False Data Injection Attacks in Power Systems Using a Secured-Sensors and Graph-Based Method

Morgenstern,

Dabush,

Kim

et al. 2023

Lecture Notes in Computer Science

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Detection of False Data Injection Attacks in Unobservable Power Systems by Laplacian Regularization

Cited by 3 publications

References 25 publications

State Estimation in Partially Observable Power Systems via Graph Signal Processing Tools

State Estimation in Partially Observable Power Systems via Graph Signal Processing Tools

Structural-Constrained Methods for the Identification of False Data Injection Attacks in Power Systems

Invited Paper: Detection of False Data Injection Attacks in Power Systems Using a Secured-Sensors and Graph-Based Method

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