@inproceedings{88af96af7fa940f4a7d9470e15a949b4,
title = "Online detection of false data injection attacks to synchrophasor measurements: A data-driven approach",
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.",
author = "Meng Wu and Le Xie",
note = "Publisher Copyright: {\textcopyright} 2017 Proceedings of the Annual Hawaii International Conference on System Sciences. All rights reserved.; 50th Annual Hawaii International Conference on System Sciences, HICSS 2017 ; Conference date: 03-01-2017 Through 07-01-2017",
year = "2017",
language = "English (US)",
series = "Proceedings of the Annual Hawaii International Conference on System Sciences",
publisher = "IEEE Computer Society",
pages = "3194--3203",
editor = "Bui, {Tung X.} and Ralph Sprague",
booktitle = "Proceedings of the 50th Annual Hawaii International Conference on System Sciences, HICSS 2017",
}