Deakin University

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A data-driven approach to distinguish cyber-attacks from physical faults in a smart grid

conference contribution
posted on 2015-01-01, 00:00 authored by Adnan AnwarAdnan Anwar, A N Mahmood, Z Shah
Recently, there has been significant increase in interest on Smart Grid security. Researchers have proposed various techniques to detect cyber-attacks using sensor data. However, there has been little work to distinguish a cyber-attack from a power system physical fault. A serious operational failure in physical power grid may occur from the mitigation strategies if fault is wrongly classified as a cyber-attack or vice-versa. In this paper, we utilize a data-driven approach to accurately differentiate the physical faults from cyber-attacks. First, we create a realistic dataset by generating different types of faults and cyber-attacks on the IEEE 30 bus benchmark test system. Next, we provide a data-driven approach where labelled data are projected in a new low-dimensional subspace using Principal Component Analysis (PCA). Next, Sequential Minimal Optimization (SMO) based Support Vectors are trained using the new projection of the original dataset. With both simulated and practical datasets, we have observed that the proposed classification method outperforms other existing popular supervised classification approaches considering the cyber-attack and fault datasets.



ACM Special Interest Group on Information Retrieval. Conference (24th : 2015 : Melbourne, Vic.)


ACM Special Interest Group on Information Retrieval Conference


1811 - 1814


Association for Computing Machinery


Melbourne, Vic.

Place of publication

New York, N.Y.

Start date


End date






Publication classification

E1.1 Full written paper - refereed

Copyright notice

2015, ACM



Title of proceedings

CIKM 2015 : Proceedings of the 24th ACM International on Conference on Information and Knowledge Management