Accurate Traffic Classification

Abstract
The analysis of network traffic can provide important information for network operators and administrators. One of the main purposes of traffic analysis is to identify the traffic mixture the network carries. A couple of different approaches have been proposed in the literature, but none of them performs well for all different application traffic types present in the Internet. Thus, a combined method that includes the advantages of different approaches is needed, in order to provide a high level of classification completeness and accuracy. According to our best knowledge, this study is the first attempt where the currently known traffic classification methods are benchmarked on network traces captured in operational mobile networks. The pros and cons of the classification methods are analyzed, based on the experienced accuracy for different types of applications. Using the gained knowledge about the strengths and weaknesses of the existing approaches, a novel traffic classification method is proposed. The novel method is based on a complex decision mechanism, in order to provide an appropriate identification mode for each different application type. As a consequence, the ratio of the unclassified traffic becomes significantly lower. Further, the reliability of the classification improves, as the various methods validate the results of each other. The novel method is tested on several network traces, and it is shown that the proposed solution improves both the completeness and the accuracy of the traffic classification, when compared to existing methods.

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