Efficient Learning of Communication Profiles from IP Flow Records
Christian Hammerschmidt (Université du Luxembourg)
Samuel Marchal (Aalto University)
Radu State (Université du Luxembourg)
Nino Pellegrino (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Sicco Verwer (TU Delft - Electrical Engineering, Mathematics and Computer Science)
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Abstract
The task of network traffic monitoring has evolved drastically with the ever-increasing amount of data flowing in large scale networks. The automated analysis of this tremendous source of information often comes with using simpler models on aggregated data (e.g. IP flow records) due to time and space constraints. A step towards utilizing IP flow records more effectively are stream learning techniques. We propose a method to collect a limited yet relevant amount of data in order to learn a class of complex models, finite state machines, in real-time. These machines are used as communication profiles to fingerprint, identify or classify hosts and services and offer high detection rates while requiring less training data and thus being faster to compute than simple models.