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Relational Frequent Patterns Mining for Novelty Detection from Data Streams

2009; Springer Science+Business Media; Linguagem: Inglês

10.1007/978-3-642-03070-3_32

1611-3349

Michelangelo Ceci, Annalisa Appice, Corrado Appice Annalisa Malerba Donato Loglisci, Costantina Caruso, Fabio Fumarola, Carmine M. Valente, Donato Malerba,

Anomaly Detection Techniques and Applications

We face the problem of novelty detection from stream data, that is, the identification of new or unknown situations in an ordered sequence of objects which arrive on-line, at consecutive time points. We extend previous solutions by considering the case of objects modeled by multiple database relations. Frequent relational patterns are efficiently extracted at each time point, and a time window is used to filter out novelty patterns. An application of the proposed algorithm to the problem of detecting anomalies in network traffic is described and quantitative and qualitative results obtained by analyzing real stream of data collected from the firewall logs are reported.