Online detection of false data injection attacks to synchrophasor measurements: A data-driven approach

Meng Wu, Le Xie

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

This paper presents an online data-driven algorithm to detect false data injection attacks towards synchronphasor measurements. The proposed algorithm applies density-based local outlier factor (LOF) analysis to detect the anomalies among the data, which can be described as spatio-temporal outliers among all the synchrophasor measurements from the grid. By leveraging the spatio-temporal correlations among multiple time instants of synchrophasor measurements, this approach could detect false data injection attacks which are otherwise not detectable using measurements obtained from single snapshot. This algorithm requires no prior knowledge on system parameters or topology. The computational speed shows satisfactory potential for online monitoring applications. Case studies on both synthetic and real-world synchrophasor data verify the effectiveness of the proposed algorithm.

Original languageEnglish (US)
Title of host publicationProceedings of the 50th Annual Hawaii International Conference on System Sciences, HICSS 2017
EditorsTung X. Bui, Ralph Sprague
PublisherIEEE Computer Society
Pages3194-3203
Number of pages10
ISBN (Electronic)9780998133102
StatePublished - 2017
Externally publishedYes
Event50th Annual Hawaii International Conference on System Sciences, HICSS 2017 - Big Island, United States
Duration: Jan 3 2017Jan 7 2017

Publication series

NameProceedings of the Annual Hawaii International Conference on System Sciences
Volume2017-January
ISSN (Print)1530-1605

Conference

Conference50th Annual Hawaii International Conference on System Sciences, HICSS 2017
Country/TerritoryUnited States
CityBig Island
Period1/3/171/7/17

ASJC Scopus subject areas

  • Engineering(all)

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