Classification of correlated internet traffic flows
Zhang, Jun, Chen, Chao, Xiang, Yang and Zhou, Wanlei 2012, Classification of correlated internet traffic flows, in TrustCom 2012 : Proceedings of the 11th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, IEEE, Piscataway, N. J., pp. 490-496.
A critical problem for Internet traffic classification is how to obtain a high-performance statistical feature based classifier using a small set of training data. The solutions to this problem are essential to deal with the encrypted applications and the new emerging applications. In this paper, we propose a new Naive Bayes (NB) based classification scheme to tackle this problem, which utilizes two recent research findings, feature discretization and flow correlation. A new bag-of-flow (BoF) model is firstly introduced to describe the correlated flows and it leads to a new BoF-based traffic classification problem. We cast the BoF-based traffic classification as a specific classifier combination problem and theoretically analyze the classification benefit from flow aggregation. A number of combination methods are also formulated and used to aggregate the NB predictions of the correlated flows. Finally, we carry out a number of experiments on a large scale real-world network dataset. The experimental results show that the proposed scheme can achieve significantly higher classification accuracy and much faster classification speed with comparison to the state-of-the-art traffic classification methods.
Field of Research
080503 Networking and Communications 080109 Pattern Recognition and Data Mining
Socio Economic Objective
890199 Communication Networks and Services not elsewhere classified