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Classifiers for Behavioral Patterns Identification Induced from Huge Temporal Data

Bazan J. G., Szczur A., Dydo L., Wojtowicz H., Szpyrka M.

Fundamenta Informaticae

2016

Vol. 143, nr 1/2

19--34

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.