Flexible Machine Learning-Based Cyberattack Detection Using Spatiotemporal Patterns for Distribution Systems.
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 spatiotem...
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Full Text (via OSTI) |
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| Format: | eBook |
| Language: | English |
| Published: |
Argonne, Ill. : Oak Ridge, Tenn. :
Argonne National Laboratory ; Distributed by the Office of Scientific and Technical Information, U.S. Department of Energy,
2020.
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| Subjects: |
| Summary: | 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. |
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| Item Description: | Published through Scitech Connect. 03/01/2020. "Journal ID: ISSN 1949-3053." "Other: 160861." Cui, Mingjian ; Wang, Jianhui ; Chen, Bo ; USDOE Office of Cybersecurity, Energy Security, and Emergency Response (CESER) |
| Physical Description: | Size: p. 1805-1808 : digital, PDF file. |