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On the detection of cyber-events in the grid using PCA

On the detection of cyber-events in the grid using PCA The emergence of cyber systems to the realm of physical control is currently being seen in the control environment of the critical infrastructure power grid. This research describes a possible way of detecting cyber-events including malicious intrusions. Specifically, the intrusion this work examines is data manipulation or data injection. The detection mechanism used is based on information retrieval and feature identification methods. Principal component analysis, a type of feature identification method, is used to transform each observed power system instance into a new dimensional space. In this new space, detection metric is created based on the Hotelling T2 value along with a probabilistic metric to classify instances that may contain malicious activity. An experimental trusted model is derived based on a pseudo-random Monte Carlo simulation of the Newton-Raphson method for a 5-bus power system. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Critical Infrastructures Inderscience Publishers

On the detection of cyber-events in the grid using PCA

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Publisher
Inderscience Publishers
Copyright
Copyright © Inderscience Enterprises Ltd
ISSN
1475-3219
eISSN
1741-8038
DOI
10.1504/IJCIS.2017.088228
Publisher site
See Article on Publisher Site

Abstract

The emergence of cyber systems to the realm of physical control is currently being seen in the control environment of the critical infrastructure power grid. This research describes a possible way of detecting cyber-events including malicious intrusions. Specifically, the intrusion this work examines is data manipulation or data injection. The detection mechanism used is based on information retrieval and feature identification methods. Principal component analysis, a type of feature identification method, is used to transform each observed power system instance into a new dimensional space. In this new space, detection metric is created based on the Hotelling T2 value along with a probabilistic metric to classify instances that may contain malicious activity. An experimental trusted model is derived based on a pseudo-random Monte Carlo simulation of the Newton-Raphson method for a 5-bus power system.

Journal

International Journal of Critical InfrastructuresInderscience Publishers

Published: Jan 1, 2017

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