Applying Non-Nested Generalized Exemplars Classification for Cyber-Power Event and Intrusion Detection.
IEEE Transactions on Smart Grid(2018)
摘要
Non-nested generalized exemplars (NNGEs) is a state of the art data mining algorithm which uses distance between a new example and a set of exemplars for classification. The state extraction method (STEM) preprocesses power system wide area measurement system data to reduce data size while maintaining critical patterns. Together NNGE+STEM make an effective event and intrusion detection system whic...
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关键词
Data mining,Classification algorithms,Machine learning algorithms,Power system faults,Artificial neural networks,Intrusion detection
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