Network traffic classification — A comparative study of two common decision tree methods: C4.5 and Random forest

Abstract

Network traffic classification gains continuous interesting while many applications emerge on the different kinds of networks with obfuscation techniques. Decision tree is a supervised machine learning method used widely to identify and classify network traffic. In this paper, we introduce a comparative study focusing on two common decision tree methods namely: C4.5 and Random forest. The study offers comparative results in two different factors are accuracy of classification and processing time. C4.5 achieved high percentage of classification accuracy reach to 99.67 for 24000 instances while Random Forest was faster than C4.5 in term of processing time.