Early Classification of Network Traffic through Multi-classification

In this work we present and evaluate different automated combination techniques for traffic classification. We consider six intelligent combination algorithms applied to both traditional andmore recent traffic classification techniques using either packet content or statistical properties of flows. Preliminary results show that, when selecting complementary classifiers, some combination algorithms allow a further improvement – in terms of classification accuracy – over already well-performing standalone classification techniques. Moreover, our experiments show that the positive impact of combination is particularly significant when there are early-classification constraints, that is, when the classification of a flow must be obtained in its early stage (e.g. first 1 – 4 packets) in order to perform network operations online.