IP Traffic Classification for QoS Guarantees: The Independence of Packets

Abstract

The classification of IP flows according to the application that generated them has become a popular research subject in the last few years. Several recent papers based their studies on the analysis of features of flows such as the packet size and inter-arrival time, which are then used as input to classification techniques derived from various scientific areas such as pattern recognition. In this paper we analyze the impact on flow classification of a hypothesis that is often overlooked, i.e., the tenet that the features of consecutive packets of a given IP flow can be considered statistically independent. We compare two approaches, one based on a technique that considers consecutive packets statistically independent, and one that relies on the opposite assumption. These techniques are then applied to three different sets of traffic traces. Experimental results show that while assuming the independence of consecutive packets has relatively few effects on true positives, it can have a significant negative impact on the false positive and true negative rates, therefore lowering the precision of the classification process.