GridWatch: Sensor Placement and Anomaly Detection in the Electrical Grid

Hooi, Bryan; Eswaran, Dhivya; Song, Hyun Ah; Pandey, Amritanshu; Jereminov, Marko; Pileggi, Larry; Faloutsos, Christos

doi:10.1007/978-3-030-10925-7_5

Cited by 8 publications

(12 citation statements)

References 29 publications

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“…In another example, He and Zhang [48] proposed a dependency graph approach coupled with a decentralized algorithm to allow the detection and localization of faults in transmission grids. More recently, Hooi et al [52] defined several detectors based on graph metrics and an algorithm to detect anomalies (e.g., transmission line failure) in a sensor-monitored power grid.…”

Section: Energymentioning

confidence: 99%

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Private Graph Data Release: A Survey

Yang¹,

Purcell²,

Rakotoarivelo³

et al. 2021

Preprint

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The application of graph analytics to various domains have yielded tremendous societal and economical benefits in recent years. However, the increasingly widespread adoption of graph analytics comes with a commensurate increase in the need to protect private information in graph databases, especially in light of the many privacy breaches in real-world graph data that was supposed to preserve sensitive information. This paper provides a comprehensive survey of private graph data release algorithms that seek to achieve the fine balance between privacy and utility, with a specific focus on provably private mechanisms. Many of these mechanisms fall under natural extensions of the Differential Privacy framework to graph data, but we also investigate more general privacy formulations like Pufferfish Privacy that can deal with the limitations of Differential Privacy. A wide-ranging survey of the applications of private graph data release mechanisms to social networks, finance, supply chain, health and energy is also provided. This survey paper and the taxonomy it provides should benefit practitioners and researchers alike in the increasingly important area of private graph data release and analysis.

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Section: Energymentioning

confidence: 99%

“…These realistic models can then be used to test cost reduction scenarios. Finally, in addition to the aforementioned anomaly detection application, Hooi et al [52] further extends its graph-based approach to compute the optimal placement of sensors in a power grid to maximize detection probability at a lower cost.…”

Section: Energymentioning

confidence: 99%

Private Graph Data Release: A Survey

Yang¹,

Purcell²,

Rakotoarivelo³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…On the contrary, ML methods, as illustrated above, are more generally applicable; however, without basic understanding of how the real world grid operates, they are likely to perform poorly. Motivated by the pros and cons, many efforts [27] [39] have combined the benefits of the two, embedding the domain-knowledge in general ML methods. Such methods with domain knowledge have been shown to have higher performance (see [27] [39]) but still do not fully consider the dynamic nature of the electric grid.…”

Section: Background and Related Workmentioning

confidence: 99%

“…Motivated by the pros and cons, many efforts [27] [39] have combined the benefits of the two, embedding the domain-knowledge in general ML methods. Such methods with domain knowledge have been shown to have higher performance (see [27] [39]) but still do not fully consider the dynamic nature of the electric grid.…”

Section: Background and Related Workmentioning

confidence: 99%

“…Generally, these families of methods are all directly applicable to the problem described in this paper. However, these methods either do not consider the impact of graph change on the observations [21] [23] [24], or assume a static topology across the data stream [27]. This is problematic for grid problems since different grid configurations naturally cause differences in grid measurements.…”

mentioning

confidence: 99%

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Dynamic Graph-Based Anomaly Detection in the Electrical Grid

Li¹,

Pandey²,

Hooi³

et al. 2020

Preprint

Self Cite

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Given sensor readings over time from a power grid, how can we accurately detect when an anomaly occurs? A key part of achieving this goal is to use the network of power grid sensors to quickly detect, in real-time, when any unusual events, whether natural faults or malicious, occur on the power grid. Existing bad-data detectors in the industry lack the sophistication to robustly detect broad types of anomalies, especially those due to emerging cyber-attacks, since they operate on a single measurement snapshot of the grid at a time. New ML methods are more widely applicable, but generally do not consider the impact of topology change on sensor measurements and thus cannot accommodate regular topology adjustments in historical data. Hence, we propose DYNWATCH, a domain knowledge based and topology-aware algorithm for anomaly detection using sensors placed on a dynamic grid. Our approach is accurate, outperforming existing approaches by 20% or more (F-measure) in experiments; and fast, averaging less than 1.7ms per time tick per sensor on a 60K+ branch case using a laptop computer, and scaling linearly with the size of the graph.

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A survey of anomaly detection methods for power grids

Madabhushi

Dewri

2023

Int. J. Inf. Secur.

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GridWatch: Sensor Placement and Anomaly Detection in the Electrical Grid

Cited by 8 publications

References 29 publications

Private Graph Data Release: A Survey

Private Graph Data Release: A Survey

Dynamic Graph-Based Anomaly Detection in the Electrical Grid

A survey of anomaly detection methods for power grids

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