Online Detection of False Data Injection Attacks to Synchrophasor Measurements: A Data-Driven Approach

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dc.contributor.authorWu, Meng
dc.contributor.authorXie, Le
dc.date.accessioned2016-12-29T01:14:23Z
dc.date.available2016-12-29T01:14:23Z
dc.date.issued2017-01-04
dc.description.abstractThis 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.
dc.format.extent10 pages
dc.identifier.doi10.24251/HICSS.2017.389
dc.identifier.isbn978-0-9981331-0-2
dc.identifier.urihttp://hdl.handle.net/10125/41544
dc.language.isoeng
dc.relation.ispartofProceedings of the 50th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectData mining
dc.subjectfalse data injection attack
dc.subjectoutlier detection
dc.subjectsynchrophasor.
dc.titleOnline Detection of False Data Injection Attacks to Synchrophasor Measurements: A Data-Driven Approach
dc.typeConference Paper
dc.type.dcmiText

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