AI based approach to identify compromised meters in data integrity attacks on smart grid

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http://digital-library.theiet.org/content/journals/10.1049/iet-gtd.2017.0455

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10.1049/iet-gtd.2017.0455
 http://hdl.handle.net/11603/11570

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UMBC Computer Science and Electrical Engineering Department
UMBC Faculty Collection

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2017-10-02

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journal article post-print
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Kush Khanna, Bijaya Ketan Panigrahi, Anupam Joshi , AI based approach to identify compromised meters in data integrity attacks on smart grid, Volume 12, Issue 5, 02 October 2017, DOI: 10.1049/iet-gtd.2017.0455

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This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
This paper is a postprint of a paper submitted to and accepted for publication in IET Generation, Transmission & Distribution and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at the IET Digital Library.

Subjects

artificial intelligence
smart power grids
security of data
learning (artificial intelligence)
power system security
power engineering computing
cyber-threats
compromised metre identification
Knowledge engineering techniques
Data security
Power system control
Power engineering computing
false data injection attacks
data integrity attacks
smart grid
UMBC Ebiquity Research Group

Abstract

False data injection attacks can pose serious threats to the operation and control of power grid. The smarter the power grid gets, the more vulnerable it becomes to cyber attacks. Various detection methods of cyber attacks have been proposed in the literature in recent past. However, to completely alleviate the possibility of cyber threats, the compromised meters must be identified and secured. In this paper, we are presenting an Artificial Intelligence (AI) based identification method to correctly single out the malicious meters. The proposed AI based method successfully identifies the compromised meters by anticipating the correct measurements in the event of the cyber attack. NYISO load data is mapped with the IEEE 14 bus system to validate the proposed method. The efficiency of the proposed method is compared for Artificial Neural Network (ANN) and Extreme Learning Machine (ELM) based AI techniques. It is observed that both the techniques identify the corrupted meters with high accuracy.