Affiliations: [a] Interdisciplinary Centre for Computational Modelling, University of Rzeszów, Pigonia 1, 35-310 Rzeszów, Poland. [email protected] | [b] Interdisciplinary Centre for Computational Modelling, University of Rzeszów, Pigonia 1, 35-310 Rzeszów, Poland. [email protected], [email protected], [email protected] | [c] Department of Applied Computer Science, AGH University of Science and Technology, Mickiewicza 30, 30-059 Kraków, Poland and Interdisciplinary Centre for Computational Modelling, University of Rzeszów, Pigonia 1, 35-310 Rzeszów, Poland. [email protected]
Correspondence:
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Address for correspondence: Interdisciplinary Centre for Computational Modelling, University of Rzeszów, Pigonia 1, 35-310 Rzeszów, Poland
Abstract: A new method of constructing classifiers from huge volume of temporal data is proposed in the paper. The novelty of introduced method finds expression in a multi-stage approach to build 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 for packet-based network traffic anomaly detection was constructed, 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.
