TITLE:
False Data Injection Attacks Detection in Power System Using Machine Learning Method
AUTHORS:
Can Yang, Yong Wang, Yuhao Zhou, Jiongming Ruan, Wei Liu
KEYWORDS:
FIDA, Machine Learning, Outlier Detection, Unsupervised Learning
JOURNAL NAME:
Journal of Computer and Communications,
Vol.6 No.11,
November
28,
2018
ABSTRACT: 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.