Traffic Classification through Joint Distributions of Packet-Level Statistics

Alberto Dainotti, Antonio Pescapè, Hyun-chul Kim

Abstract: Interest in traffic classification, in both industry and academia, has dramatically grown in the past few years. Research is devoting great efforts to statistical approaches using robust features. In this paper we propose a classification approach based on the joint distribution of Packet Size (PS) and Inter-Packet Time (IPT) and on machine- learning algorithms. Provided results, obtained using different real traffic traces, demonstrate how the proposed approach is able to achieve high (byte) accuracy (till 98%) and how the new features we introduced show properties of robustness, which suggest their use in the design of classification/identification approaches robust to traffic encryption and protocol obfuscation.