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Appl. Sci. 2018, 8(5), 772; https://doi.org/10.3390/app8050772

Covert Cyber Assault Detection in Smart Grid Networks Utilizing Feature Selection and Euclidean Distance-Based Machine Learning

School of Electrical Engineering, University of Ulsan, Ulsan 44610, Korea
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Received: 11 April 2018 / Revised: 3 May 2018 / Accepted: 9 May 2018 / Published: 12 May 2018
(This article belongs to the Section Computer Science and Electrical Engineering)
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Abstract

Communications technologies are an integral part of efficient monitoring and reliable control in smart grids, but enhanced reliance on these technologies heightens the risk of cyber assaults. Recently, a new type of stealth, or covert, assault in smart grid networks has been discovered, which cannot be ascertained by legacy bad-data detectors using state estimation. Due to the delay-sensitive nature of smart grid networks, swift detection of abnormal changes is immensely desired. In this paper, we propose two Euclidean distance-based anomaly detection schemes for covert cyber-assault detection in smart grid communications networks. The first scheme utilizes unsupervised-learning over unlabeled data to detect outliers or deviations in the measurements. The second scheme employs supervised-learning over labeled data to detect the deviations in the measurements. Unlike the classic detection test, the proposed schemes tackle an unknown sample with low computational complexity, leading to a shorter decision time. To improve detection accuracy and further reduce the computational complexity and the associated time delay, we employ a genetic algorithm-based feature selection method to choose the distinguishing optimal feature data subset as input to both of the proposed schemes. The evaluation is carried out through the standard IEEE 14-bus, 39-bus, 57-bus and 118-bus test systems. Simulation results show that compared to the existing feature extraction-based detection schemes, the proposed schemes show significant improvement in covert cyber deception assault-detection accuracy. View Full-Text
Keywords: anomaly detection; cyber assaults; Euclidean distance; feature selection; genetic algorithm; smart grids; state estimation anomaly detection; cyber assaults; Euclidean distance; feature selection; genetic algorithm; smart grids; state estimation
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Ahmed, S.; Lee, Y.; Hyun, S.-H.; Koo, I. Covert Cyber Assault Detection in Smart Grid Networks Utilizing Feature Selection and Euclidean Distance-Based Machine Learning. Appl. Sci. 2018, 8, 772.

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