期刊
IEEE ACCESS
卷 5, 期 -, 页码 26022-26033出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2017.2769099
关键词
Data analytical; false data injection; cyber-physical attack; smart grid
False data injection cyber-physical threat is a typical integrity attack in modern smart grids. These days, data analytical methods have been employed to mitigate false data injection attacks (FDIAs), especially when large scale smart grids generate huge amounts of data. In this paper, a novel data analytical method is proposed to detect FDIAs based on data-centric paradigm employing the margin setting algorithm (MSA). The performance of the proposed method is demonstrated through simulation using the six-bus power network in a wide area measurement system environment, as well as experimental data sets. Two FDIA scenarios, playback attack and time attack, are investigated. Experimental results are compared with the support vector machine (SVM) and artificial neural network (ANN). The results indicate that MSA yields better results in terms of detection accuracy than both the SVM and ANN when applied to FDIA detection.
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