False Data Injection Attacks Detection in Power System Using Machine Learning Method

• 作者: Can Yang Jiongming Ruan Wei Liu Yong Wang Yuhao Zhou
• 来源期刊: 电脑和通信(英文)
• 学科: 工学
• 关键词: FIDA MACHINE LEARNING OUTLIER DETECTION UNSUPERVISED LEARNING
• 年，卷（期）: 2018,（11）
• 页码范围: 276-286
• 页数: 11页
• 分类号: TP39

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.