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Effective detection of cyber attack in a cyber-physical power grid system.

Otokwala, Uneneibotejit; Petrovski, Andrei; Kalutarage, Harsha

Authors

Uneneibotejit Otokwala

Harsha Kalutarage



Contributors

Kohei Arai
Editor

Abstract

Advancement in technology and the adoption of smart devices in the operation of power grid systems have made it imperative to ensure adequate protection for the cyber-physical power grid system against cyber-attacks. This is because, contemporary cyber-attack landscapes have made devices’ first line of defense (i.e. authentication and authorization) hardly enough to withstand the attacks. To detect these attacks, this paper proposes a detection methodology based on Machine Learning techniques. The dataset used in this experiment was obtained from the synchrophasor measurements of data logs from snort, simulated control panels and relays of a smart power grid transmission system. After the preprocessing of the dataset, it was then scaled and analyzed before the fitting of - Random Forest, Support Vector Machine, Linear Discriminant Analysis and K-Nearest Neighbor algorithms. The fitting of the different classifiers was done in order to find the algorithm with the best output. Upon the completion of the experiment, the results of classifiers were tabulated and the result of the Random Forest model was the most effective with an accuracy of 92% and a significantly low rate of misclassification. The Random Forest model also shows a high percentage of the true positive rate that is critical to the security issue.

Citation

OTOKWALA, U., PETROVSKI, A. and KALUTARAGE, H. 2021. Effective detection of cyber attack in a cyber-physical power grid system. In Arai, K. (ed) Advances in information and communication: proceedings of Future of information and communication conference (FICC 2021), 29-30 April 2021, Vancouver, Canada. Advances in intelligent systems and computing, 1363. Cham: Springer [online], 1, pages 812-829. Available from: https://doi.org/10.1007/978-3-030-73100-7_57

Conference Name 2021 Future of information and communication conference (FICC 2021)
Conference Location Vancouver, Canada
Start Date Apr 29, 2021
End Date Apr 30, 2021
Acceptance Date Apr 13, 2021
Online Publication Date Apr 13, 2021
Publication Date Dec 31, 2021
Deposit Date Jun 4, 2021
Publicly Available Date Apr 14, 2022
Publisher Springer
Volume 1
Pages 812-829
Series Title Advances in Intelligent Systems and Computing
Series Number 1363
Series ISSN 2194-5357
Book Title Advances in information and communication: proceedings of Future of information and communication conference (FICC 2021)
ISBN 9783030730994
DOI https://doi.org/10.1007/978-3-030-73100-7_57
Keywords Cyber-attack detection; Smart grid system; True positive rate
Public URL https://rgu-repository.worktribe.com/output/1352642