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Electronics 2016, 5(4), 82; doi:10.3390/electronics5040082

Gaussian Mixture Modeling for Detecting Integrity Attacks in Smart Grids

1
Politecnico di Milano, Department of Electronics, Information and Bioengineering, Milan 20133, Italy
2
European Commission, Joint Research Center, Ispra 21027, Italy
*
Author to whom correspondence should be addressed.
Academic Editors: Alfredo Vaccaro and Jin (Wei)Kocsis
Received: 17 September 2016 / Revised: 2 November 2016 / Accepted: 15 November 2016 / Published: 23 November 2016
(This article belongs to the Special Issue Smart Grid Cyber Security)
View Full-Text   |   Download PDF [1099 KB, uploaded 23 November 2016]   |  

Abstract

The thematics focusing on inserting intelligence in cyber-physical critical infrastructures (CI) have been receiving a lot of attention in the recent years. This paper presents a methodology able to differentiate between the normal state of a system composed of interdependent infrastructures and states that appear to be normal but the system (or parts of it) has been compromised. The system under attack seems to operate properly since the associated measurements are simply a variation of the normal ones created by the attacker, and intended to mislead the operator while the consequences may be of catastrophic nature. Here, we propose a holistic modeling scheme based on Gaussian mixture models estimating the probability density function of the parameters coming from linear time invariant (LTI) models. LTI models are approximating the relationships between the datastreams coming from the CI. The experimental platform includes a power grid simulator of the IEEE 30 bus model controlled by a cyber network platform. Subsequently, we implemented a wide range of integrity attacks (replay, ramp, pulse, scaling, and random) with different intensity levels. An extensive experimental campaign was designed and we report satisfying detection results. View Full-Text
Keywords: critical infrastructure protection; linear time invariant modeling; hidden Markov model; fault diagnosis; cyber-physical system; Gaussian mixtures critical infrastructure protection; linear time invariant modeling; hidden Markov model; fault diagnosis; cyber-physical system; Gaussian mixtures
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Ntalampiras, S.; Soupionis, Y. Gaussian Mixture Modeling for Detecting Integrity Attacks in Smart Grids. Electronics 2016, 5, 82.

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