A traffic classification approach based on characteristics of subflows and ensemble learning

Recently, network traffic classification has attracted a great deal of attention among researchers. In this paper, we proposed a traffic classification approach based on characteristics of subflows and ensemble learning. Aiming at neutralization of unstable network environment as well as taking advantage of ensemble learning, we divided the traffic flows into different subflows in order to reduce the affection of time. Moreover, we develop truncation method on flows for real-time processing and an aggregation machine learning method based on accuracy of each classifier to different applications. Finally, the experimental results based on actual traffic traces collected from the campus network of Xian Jiaotong University verify the effectiveness of our methods.