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Hink, R.C.B., Beaver, J.M., Buchner, M.A., Morris, T., Adhikari, U. and Pan, S.Y. (2014) Machine Learning for Power System Disturbance and Cyber-Attack Discrimination. 2014 7th International Symposium on Resilient Control Systems (ISRCS), Denver, CO, 19-21 August 2014.

has been cited by the following article:

• 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.