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.
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