Flexible Machine Learning-Based Cyberattack Detection Using Spatiotemporal Patterns for Distribution Systems

被引：51

作者：

Cui, Mingjian ^{[1
]}

Wang, Jianhui ^{[1
]}

Chen, Bo ^{[2
]}

机构：

[1] Southern Methodist Univ, Dept Elect & Comp Engn, Dallas, TX 75275 USA

[2] Argonne Natl Lab, Energy Syst Div, Argonne, IL 60439 USA

来源：

IEEE TRANSACTIONS ON SMART GRID | 2020年 / 11卷 / 02期

关键词：

Cyberattack detection; distribution systems; graph Laplacian; machine learning; spatiotemporal patterns; ANOMALY DETECTION;

D O I：

10.1109/TSG.2020.2965797

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This letter develops a flexible machine learning detection method for cyberattacks in distribution systems considering spatiotemporal patterns. Spatiotemporal patterns are recognized by the graph Laplacian based on system-wide measurements. A flexible Bayes classifier (BC) is used to train spatiotemporal patterns which could be violated when cyberattacks occur. Cyberattacks are detected by using flexible BCs online. The effectiveness of the developed method is demonstrated through standard IEEE 13- and 123-node test feeders.

引用

页码：1805 / 1808

页数：4

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