Flexible Machine Learning-Based Cyberattack Detection Using Spatiotemporal Patterns for Distribution Systems
Journal Article
·
· IEEE Transactions on Smart Grid
- Southern Methodist Univ., Dallas, TX (United States)
- Argonne National Lab. (ANL), Argonne, IL (United States)
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.
- Research Organization:
- Argonne National Laboratory (ANL), Argonne, IL (United States)
- Sponsoring Organization:
- USDOE Office of Cybersecurity, Energy Security, and Emergency Response (CESER)
- Grant/Contract Number:
- AC02-06CH11357
- OSTI ID:
- 1812898
- Journal Information:
- IEEE Transactions on Smart Grid, Journal Name: IEEE Transactions on Smart Grid Journal Issue: 2 Vol. 11; ISSN 1949-3053
- Publisher:
- IEEECopyright Statement
- Country of Publication:
- United States
- Language:
- English
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