Blind False Data Injection Attack Using PCA Approximation Method in Smart Grid

Yu, Zong Han; Chin, Wen‐Long

doi:10.1109/tsg.2014.2382714

Cited by 283 publications

(143 citation statements)

References 28 publications

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“…In this section, the proposed approach is applied and investigated on the IEEE 14-bus test grid. This test grid is often used in the context of FDI attack detection [27], [29], [30] and attack construction [35], [38]. The voltage angles and magnitudes of this grid are calculated from the AC power flow solver implemented by the pandapower-tool [55].…”

Section: Case Studymentioning

confidence: 99%

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Detection of False Data Injection Attacks in Smart Grids Based on Graph Signal Processing

Drayer

Routtenberg

2020

IEEE Systems Journal

123

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Section: Case Studymentioning

confidence: 99%

“…Most of the aforementioned works, notably [27]- [32], [35], rely on the linearized DC model to describe the electrical behavior of the power system. However, since the DC model is only a simplification of the more realistic and more accurate AC model, this is a significant shortcoming with regard to the vulnerability analysis [11].…”

mentioning

confidence: 99%

Detection of False Data Injection Attacks in Smart Grids Based on Graph Signal Processing

Drayer

Routtenberg

2020

IEEE Systems Journal

123

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“…Note that direct application of subspace estimation algorithm as proposed in [5], [6], [7] (Algorithm 1) uses all N estimated 1 Limiting eigen value distribution is the distribution of eigen values of Σz when M, T → ∞, M/T = p > 0. eigen modes for the construction of the FDI attack. However, following the application of RMT spiked model results, it is clear that eigen modes for which µ i < √ p cannot be recovered from the sample covariance matrix, and hence, must not be used in the construction of FDI attack.…”

Section: Data-driven Fdi Attacks With Limited Number Of Measurementsmentioning

confidence: 99%

Trade-offs in Data-Driven False Data Injection Attacks Against the Power Grid

Lakshminarayana

Wen

Yau

2018

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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We address the problem of constructing false data injection (FDI) attacks that can bypass the bad data detector (BDD) of a power grid. The attacker is assumed to have access to only power flow measurement data traces (collected over a limited period of time) and no other prior knowledge about the grid. Existing related algorithms are formulated under the assumption that the attacker has access to measurements collected over a long (asymptotically infinite) time period, which may not be realistic. We show that these approaches do not perform well when the attacker has a limited number of data samples only. We design an enhanced algorithm to construct FDI attack vectors in the face of limited measurements that can nevertheless bypass the BDD with high probability. The algorithm design is guided by results from random matrix theory. Furthermore, we characterize an important trade-off between the attack's BDD-bypass probability and its sparsity, which affects the spatial extent of the attack that must be achieved. Extensive simulations using data traces collected from the MATPOWER simulator and benchmark IEEE bus systems validate our findings. S. Lakshminarayana is with the

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“…Unlike the studies discussed before, Yu and Chin [18] proposed a principal component analysis (PCA) based method to design blind FDI attacks, which did not need any prior knowledge of Jacobian matrix in smart grid. Furthermore, Anwar and Mahmood [19] clarified that the PCA based blind attack strategy was only valid for the measurements with Gaussian noises.…”

Section: Introductionmentioning

confidence: 99%

A Novel Covert Agent for Stealthy Attacks on Industrial Control Systems Using Least Squares Support Vector Regression

Xie

Wang

2018

Journal of Electrical and Computer Engineering

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Research on stealthiness has become an important topic in the field of data integrity (DI) attacks. To construct stealthy DI attacks, a common assumption in most related studies is that attackers have prior model knowledge of physical systems. In this paper, such assumption is relaxed and a covert agent is proposed based on the least squares support vector regression (LSSVR). By estimating a plant model from control and sensory data, the LSSVR-based covert agent can closely imitate the behavior of the physical plant. Then, the covert agent is used to construct a covert loop, which can keep the controller's input and output both stealthy over a finite time window. Experiments have been carried out to show the effectiveness of the proposed method.

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Blind False Data Injection Attack Using PCA Approximation Method in Smart Grid

Cited by 283 publications

References 28 publications

Detection of False Data Injection Attacks in Smart Grids Based on Graph Signal Processing

Detection of False Data Injection Attacks in Smart Grids Based on Graph Signal Processing

Trade-offs in Data-Driven False Data Injection Attacks Against the Power Grid

A Novel Covert Agent for Stealthy Attacks on Industrial Control Systems Using Least Squares Support Vector Regression

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