摘要 : 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 proce... 展开 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. 收起