Fast Sequence Component Analysis for Attack Detection in Smart Grid

Jordan Landford, Rich Meier, Richard Barella, Scott Wallace, Xinghui Zhao, Eduardo Cotilla-Sanchez, Robert B. Bass

2016

Abstract

Modern power systems have begun integrating synchrophasor technologies into part of daily operations. Given the amount of solutions offered and the maturity rate of application development it is not a matter of “if” but a matter of “when” in regards to these technologies becoming ubiquitous in control centers around the world. While the benefits are numerous, the functionality of operator-level applications can easily be nullified by injection of deceptive data signals disguised as genuine measurements. Such deceptive action is a common precursor to nefarious, often malicious activity. A correlation coefficient characterization and machine learning methodology are proposed to detect and identify injection of spoofed data signals. The proposed method utilizes statistical relationships intrinsic to power system parameters, which are quantified and presented. Several spoofing schemes have been developed to qualitatively and quantitatively demonstrate detection capabilities.

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Paper Citation


in Harvard Style

Landford J., Meier R., Barella R., Wallace S., Zhao X., Cotilla-Sanchez E. and Bass R. (2016). Fast Sequence Component Analysis for Attack Detection in Smart Grid . In Proceedings of the 5th International Conference on Smart Cities and Green ICT Systems - Volume 1: SMARTGREENS, ISBN 978-989-758-184-7, pages 225-232. DOI: 10.5220/0005860302250232


in Bibtex Style

@conference{smartgreens16,
author={Jordan Landford and Rich Meier and Richard Barella and Scott Wallace and Xinghui Zhao and Eduardo Cotilla-Sanchez and Robert B. Bass},
title={Fast Sequence Component Analysis for Attack Detection in Smart Grid},
booktitle={Proceedings of the 5th International Conference on Smart Cities and Green ICT Systems - Volume 1: SMARTGREENS,},
year={2016},
pages={225-232},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005860302250232},
isbn={978-989-758-184-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 5th International Conference on Smart Cities and Green ICT Systems - Volume 1: SMARTGREENS,
TI - Fast Sequence Component Analysis for Attack Detection in Smart Grid
SN - 978-989-758-184-7
AU - Landford J.
AU - Meier R.
AU - Barella R.
AU - Wallace S.
AU - Zhao X.
AU - Cotilla-Sanchez E.
AU - Bass R.
PY - 2016
SP - 225
EP - 232
DO - 10.5220/0005860302250232