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

dc.contributor.author Wu, Meng
dc.contributor.author Xie, Le
dc.date.accessioned 2016-12-29T01:14:23Z
dc.date.available 2016-12-29T01:14:23Z
dc.date.issued 2017-01-04
dc.description.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.
dc.format.extent 10 pages
dc.identifier.doi 10.24251/HICSS.2017.389
dc.identifier.isbn 978-0-9981331-0-2
dc.identifier.uri http://hdl.handle.net/10125/41544
dc.language.iso eng
dc.relation.ispartof Proceedings of the 50th Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Data mining
dc.subject false data injection attack
dc.subject outlier detection
dc.subject synchrophasor.
dc.title Online Detection of False Data Injection Attacks to Synchrophasor Measurements: A Data-Driven Approach
dc.type Conference Paper
dc.type.dcmi Text
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