Study of Monitoring False Data Injection Attacks Based on Machine-learning in Electric Systems
作者: Baoyi WangYadong ZhaoShaomin ZhangBihe Li
刊名: Journal of Electronics and Information Science, 2017, Vol.2 (2)
来源数据库: Clausius Scientific Press
DOI: 10.23977/jeis.2017.22013
关键词: FDIAMachine LearningSupervised Learning
原始语种摘要: False data injected by hackers can interfere with power system state estimation and pose a great threat to the safe and reliable operation of modern power systems (FDIA). The traditional bad data detection method can not effectively detect such attacks. In this paper, by extracting relevant power system measurement characteristic value and use the historical data as the sample, using three classical machine learning algorithms (Perceptron, KNN, SVM) of false data injection attack detection, and respectively in IEEE-9, IEEE-57, IEEE-118 simulation platform for test, verify the supervised machine learning algorithm is applied to the validity of false data injection attack detection.
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  • Data 数据
  • Injection 喷射注射注频(把信号加到电路或电子管)(卫星)射入轨道
  • learning 学识
  • attack 侵蚀
  • machine 机器
  • platform 台地
  • threat 威胁
  • extracting 选取
  • false 错误的
  • SVM 共享虚拟存储器