Detecting stealthy false data injection attacks in the smart grid using ensemble-based machine learning

被引:72
作者
Das, Mohammad Ashrafuzzaman [1 ]
Das, Saikat [2 ]
Chakhchoukh, Yacine [3 ]
Shiva, Sajjan [2 ]
Sheldon, Frederick T. [1 ]
机构
[1] Univ Idaho, Comp Sci, Moscow, ID 83843 USA
[2] Univ Memphis, Comp Sci, Memphis, TN 38152 USA
[3] Univ Idaho, Elect & Comp Engn, Moscow, ID 83843 USA
关键词
Smart grid security; Stealthy false data injection attack; Ensemble-based machine learning; Cyber-physical system security; Critical infrastructure protection; STATE ESTIMATION; NETWORKS;
D O I
10.1016/j.cose.2020.101994
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Stealthy false data injection attacks target state estimation in energy management systems in smart power grids to adversely affect operations of the power transmission systems. This paper presents a data-driven machine learning based scheme to detect stealthy false data injection attacks on state estimation. The scheme employs ensemble learning, where multiple classifiers are used and decisions by individual classifiers are further classified. Two ensembles are used in this scheme, one uses supervised classifiers while the other uses unsupervised classifiers. The scheme is validated using simulated data on the standard IEEE 14-bus system. Experimental results show that the performance of both supervised individual and ensemble models are comparable. However, for unsupervised models, the ensembles performed better than the individual classifiers. (C) 2020 Elsevier Ltd. All rights reserved.
引用
收藏
页数:15
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