Boosting statistical application identification by flow correlation
نویسندگان
چکیده
In this paper, we propose a new online method for traffic classification that combines the statistical and host-based approaches in order to construct a robust and precise method for early Internet traffic identification. We use the packet size as the main feature for the classification and we benefit from the traffic profile of the host (i.e., which application and how much) to decide in favor of this or that application. This profile is updated online based on the result of the classification of previous flows originated by or addressed to the same host. We evaluate our method on real traces using several applications. The results show that leveraging the traffic pattern of the host ameliorates the performance of statistical methods. They also prove the capacity of our solution to derive profiles for the traffic of Internet hosts and to identify the services they provide.
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