Toward Fine-grained Traffic Classification

A decade of research on traffic classification has provided various methodologies to investigate the traffic composition in data communication networks. Many variants or combinations of such methodologies have been introduced continuously to improve the classification accuracy and efficiency. However, the level of classification details is often bounded to identifying protocols or applications in use. In this article, we propose a fine-grained traffic classification scheme based on the analysis of existing classification methodologies. This scheme allows to classify traffic according to the functionalities in an application. In particular, we present a traffic classifier which utilizes a document retrieval technique and applies multiple signatures to detect the peer-to-peer application traffic according to different functionalities in it. We show that the proposed scheme can provide more in-depth classification results for analyzing user contexts.This research was supported by the World Class University (WCU) program through the National Research Foundation of Korea funded by the Ministry of Education, Science and Technology (R31-2010-000-10100-0) and the Korea Communications Commission (KCC) under the Novel Study on Highly Manageable Network and Service Architecture for the New Generation support program supervised by the Korea Communications Agency (KCA; KCA-2011-10921-05003)