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Classifiers for Behavioral Patterns Identification Induced from Huge Temporal Data
(Humboldt University, 2014) Bazan, Jan G.; Szpyrka, Marcin; Szczur, Adam; Dydo, Łukasz; Wojtowicz, Hubert
A new method of constructing classifiers from huge volume of temporal data is proposed in the paper. The novelty of introduced method lies in a multi-stage approach to constructing hierarchical classifiers that combines process mining, feature extraction based on temporal patterns and constructing classifiers based on a decision tree. Such an approach seems to be practical when dealing with huge volume of temporal data. As a proof of concept a system has been constructed for packet-based network traffic anomaly detection, where anomalies are represented by spatio-temporal complex concepts and called by behavioral patterns. Hierarchical classifiers constructed with the new approach turned out to be better than ”flat” classifiers based directly on captured network traffic data.