Detection of False Data Injection Attacks in Power Systems with Graph Fourier Transform

Drayer, Elisabeth; Routtenberg, Tirza

doi:10.1109/globalsip.2018.8646454

Cited by 35 publications

(23 citation statements)

References 27 publications

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“…The proposed approach for FDI attack detection can be applied to the DC model. The details and a full algorithm for the DC model case can be found in our preliminary work [46].…”

Section: Fdi Attack Detection For the DC Modelmentioning

confidence: 99%

“…Finally, we conduct numerical simulations on the IEEE 14-bus test case that demonstrate that the proposed method is able to detect previously undetectable attacks. A preliminary version of this approach, which is limited to the DC model and to the detection of voltage angles and without the derivations of the cutoff frequency of the filter and the detection threshold has been published in [46].…”

mentioning

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

Self Cite

111

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“…The proposed approach for FDI attack detection can be applied to the DC model. The details and a full algorithm for the DC model case can be found in our preliminary work [46].…”

Section: Fdi Attack Detection For the DC Modelmentioning

confidence: 99%

mentioning

confidence: 99%

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

Drayer

Routtenberg

2020

IEEE Systems Journal

Self Cite

111

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show abstract

“…False data injection is a major threat for smart grids; simplistic false data injection attacks can be detected by examining whether the reported data comply with the laws of physics, collating the data sourced from the smart grid's data channel with the measurements sourced from the smart grid's energy channel [13]. [43] proposes a model taking into account more features of the power channel and exhibiting increased potential to detect false data injection attacks, even when realistic data are injected.…”

Section: Detecting and Mitigating Attacks On Integritymentioning

confidence: 99%

Threat landscape for smart grid systems

Mathas

Grammatikakis

Vassilakis

et al. 2020

Proceedings of the 15th International Conference on Availability, Reliability and Security

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Smart Grids are energy delivery networks, constituting an evolution of power grids, in which a bidirectional flow between power providers and consumers is established. These flows support the transfer of electricity and information, in order to support automation actions in the context of the energy delivery network. Insofar, many smart grid implementations and implementation proposals have emerged, with varying degrees of feature delivery and sophistication. While smart grids offer many advantages, their distributed nature and information flow streams between energy producers and consumers enable the launching of a number of attacks against the smart grid infrastructure, where the related consequences may range from economic loss to complete failure of the smart grid. In this paper, we survey the threat landscape of smart grids, identifying threats that are specific to this infrastructure, providing an assessment of the severity of the consequences of each attack type, discerning features that can be utilized to detect attacks and listing methods that can be used to mitigate them.

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“…We construct classifiers based on the graph-spectral information generated by the sGFT and the conventional GFT along with the aforementioned GSO-construction methods. The approach for constructing the anomaly detector is similar to those in [35], [50], [51]. In particular, [35] and [50] compute the respective high-frequency components from the adjacency matrix and the graph Laplacian.…”

Section: A Anomaly Detection Taskmentioning

confidence: 99%

“…The approach for constructing the anomaly detector is similar to those in [35], [50], [51]. In particular, [35] and [50] compute the respective high-frequency components from the adjacency matrix and the graph Laplacian. On the other hand, in [51], the Laplacian eigenvectors are obtained from a constrained optimization problem that enforces properties not considered here, such as sparsity.…”

Section: A Anomaly Detection Taskmentioning

confidence: 99%

Extended Adjacency and Scale-Dependent Graph Fourier Transform via Diffusion Distances

Elias

Martins

Werner

2020

IEEE Trans. on Signal and Inf. Process. over Networks

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This paper proposes the augmentation of the adjacency model of networks for graph signal processing. It is assumed that no information about the network is available, apart from the initial adjacency matrix. In the proposed model, additional edges are created according to a Markov relation imposed between nodes. This information is incorporated into the extended-adjacency matrix as a function of the diffusion distance between nodes. The diffusion distance measures similarities between nodes at a certain diffusion scale or time, and is a metric adopted from diffusion maps. Similarly, the proposed extendedadjacency matrix depends on the diffusion scale, which enables the definition of a scale-dependent graph Fourier transform. We conduct theoretical analyses of both the extended adjacency and the corresponding graph Fourier transform and show that different diffusion scales lead to different graph-frequency perspectives. At different scales, the transform discriminates shifted ranges of signal variations across the graph, revealing more information on the graph signal when compared to traditional approaches. The scale-dependent graph Fourier transform is applied for anomaly detection and is shown to outperform the conventional graph Fourier transform.

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Detection of False Data Injection Attacks in Power Systems with Graph Fourier Transform

Cited by 35 publications

References 27 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

Threat landscape for smart grid systems

Extended Adjacency and Scale-Dependent Graph Fourier Transform via Diffusion Distances

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