WeChat text messages service flow traffic classification using machine learning technique

In this era of information technology, Network Traffic Classification is a very important and hot topic from the perspective of network security and management due to substantial use of dynamic applications. Numerous research models have been proposed in Network Traffic Classification to classify different types of applications and achieve significant accuracy results. However, no work has been done to classify WeChat messages flow traffic. WeChat is a free instant messaging application. Hence, it is very important to classify WeChat text messages traffic. In this paper, we classify WeChat messages flows traffic using two different data sets, which are first captured using Wireshark tool from two different locations network environments, Harbin Institute of Technology Lab and Jinyuan Hotel and then 50 features are extracted from captured traffic. After that four machine learning algorithms SVM, C4.5, Bayes Net and Naïve Byes are applied to classify the WeChat text messages traffic. Experimental results show that all classifiers give very high accuracy results using two different data sets. Using Jinyuan data set SVM and C4.5 decision tree algorithm give 100% accuracy result as compared to Bayes Net and Naïve Bayes algorithm and using Harbin Institute of Technology Lab data set all classifiers give 99.7% high accuracy results.