Please use this identifier to cite or link to this item: http://hdl.handle.net/10125/41544

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

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Title: Online Detection of False Data Injection Attacks to Synchrophasor Measurements: A Data-Driven Approach
Authors: Wu, Meng
Xie, Le
Keywords: Data mining
false data injection attack
outlier detection
synchrophasor.
Issue Date: 04 Jan 2017
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.
Pages/Duration: 10 pages
URI/DOI: http://hdl.handle.net/10125/41544
ISBN: 978-0-9981331-0-2
DOI: 10.24251/HICSS.2017.389
Rights: Attribution-NonCommercial-NoDerivatives 4.0 International
Appears in Collections:Resilent Networks Minitrack



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