WeChat Text and Picture Messages Service Flow Traffic Classification Using Machine Learning Technique

Abstract

Network Traffic Classification carries great importance for both internet service providers (ISPs) and quality of services (QoSs) management. During the last two decades, a lot of machine learning models have been proposed and applied on different types of real time applications to classify their real time traffic and obtain very proficient accuracy results. However, no research has been done on WeChat text and picture messages traffic classification. In this paper, WeChat text and picture messages traffics are classified using two different types of datasets and 4 well-known machine learning algorithms. These two datasets, Harbin Institute of Technology (HIT) and Dorm13, are collected from two different network environments. Having captured the traffic 50 features, they are extracted respectively. Thereafter, well-known four machine learning algorithms C4.5 decision tree, Bayes Net, Naïve Bayes and SVM are used to classify WeChat text and picture messages traffic. Experimental result analysis show that using HIT data set all the applied machine learning classifiers classify WeChat text and picture messages traffic very accurately as compared to Dorm13 dataset. Using HIT dataset, all ML classifier perform very well, but C4.5 and SVM are the ones that give very effective accuracy results of 99.91% and 99.57% respectively as compared to other ML classifiers.