Network Traffic Classification with Improved Random Forest
Chao Wang, Tongge Xu, Xi Wen Qin · 2015
Accurate network traffic classification is significant to numerous network activities, such as QoS and network management etc. While port-based or payload-based classification methods are becoming more and more difficult, Machine Learning methods are promising in many aspects. In this paper, we improve the standard Random Forest by setting the variable selection probability according to the importance of the corresponding variable to classify network traffic. Our test results show that the Improved Random Forest has better classification performance. And it takes less time to build the model.