Flexible Machine Learning-Based Cyberattack Detection Using Spatiotemporal Patterns for Distribution Systems
Mingjian Cui, Jianhui Wang, Bo Chen
Abstract
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 13and 123-node test feeders.
Topics & Concepts
Computer scienceNaive Bayes classifierArtificial intelligenceClassifier (UML)Machine learningAnomaly detectionBayes' theoremData miningPattern recognition (psychology)Support vector machineBayesian probabilitySmart Grid Security and ResilienceElectricity Theft Detection TechniquesNetwork Security and Intrusion Detection