文章基本信息

标题：False Data Injection Attacks Detection in Power System Using Machine Learning Method
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作者：Can Yang ; Yong Wang ; Yuhao Zhou 等
期刊名称：Journal of Computer and Communications
印刷版ISSN：2327-5219
电子版ISSN：2327-5227
出版年度：2018
卷号：6
期号：11
页码：276-286
DOI：10.4236/jcc.2018.611025
语种：English
出版社：Scientific Research Publishing
摘要：False data injection attacks (FIDAs) against state estimation in power system are a problem that could not be effectively solved by traditional methods. In this paper, we use four outlier detection methods, namely one-Class SVM, Robust covariance, Isolation forest and Local outlier factor method from machine learning area in IEEE14 simulation platform for test and compare their performance. The accuracy and precision were estimated through simulation to observe the classification effect.
关键词：FIDA;Machine Learning;Outlier Detection;Unsupervised Learning