Classifiers for Behavioral Patterns Identification Induced from Huge Temporal Data

Obrazek miniatury
Bazan, Jan. G.
Szpyrka, Marcin
Szczur, Adam
Dydo, Łukasz
Wojtowicz, Hubert
Tytuł czasopisma
Tytuł tomu
Humboldt University
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.
Praca opublikowana w: Bazan, J., G., Szpyrka, M., Szczur, A., Dydo, L., Wojtowicz, H.: Classifiers for Behavioral Patterns Identification Induced from Huge Temporal Data, In Proceedings of the Workshop on Concurrency, Specification and Programming (CS&P 2014), Chemnitz, Germany, 2014, September 29-October 1, volume 245 of Informatik-Bericht, pages 22-33, Humboldt University, 2014.
Słowa kluczowe
classifiers , huge temporal data , temporal patterns , state graphs , behavioral patterns , LTL temporal logic