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Mobile Information Systems
Volume 2017, Article ID 3146868, 22 pages
https://doi.org/10.1155/2017/3146868
Research Article

Effective Packet Number for 5G IM WeChat Application at Early Stage Traffic Classification

School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China

Correspondence should be addressed to Xiangzhan Yu; nc.ude.tih@nahzgnaixuy

Received 28 October 2016; Revised 29 December 2016; Accepted 15 January 2017; Published 14 February 2017

Academic Editor: Michal Choras

Copyright © 2017 Muhammad Shafiq and Xiangzhan Yu. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Accurate network traffic classification at early stage is very important for 5G network applications. During the last few years, researchers endeavored hard to propose effective machine learning model for classification of Internet traffic applications at early stage with few packets. Nevertheless, this essential problem still needs to be studied profoundly to find out effective packet number as well as effective machine learning (ML) model. In this paper, we tried to solve the above-mentioned problem. For this purpose, five Internet traffic datasets are utilized. Initially, we extract packet size of 20 packets and then mutual information analysis is carried out to find out the mutual information of each packet on flow type. Thereafter, we execute 10 well-known machine learning algorithms using crossover classification method. Two statistical analysis tests, Friedman and Wilcoxon pairwise tests, are applied for the experimental results. Moreover, we also apply the statistical tests for classifiers to find out effective ML classifier. Our experimental results show that 13–19 packets are the effective packet numbers for 5G IM WeChat application at early stage network traffic classification. We also find out effective ML classifier, where Random Forest ML classifier is effective classifier at early stage Internet traffic classification.