### Network traffic classification based on combination of multi-classifiers

KONG Beibei1, TANG Xuewen2, WANG Weihan1

1. 1.School of Computer Science, Chongqing University, Chongqing 400030, China
2.Center of Information and Network, Chongqing University, Chongqing 400030, China
• Online:2013-09-01 Published:2013-09-13

### 一种多分类器联合的集成网络流量分类方法

1. 1.重庆大学 计算机学院，重庆 400030
2.重庆大学 信息与网络管理中心，重庆 400030

Abstract: Traditionally, in the area of the network traffic classification, there exists a problem that single learning algorithm lacks classification accuracy and is incapable of adapting to the dynamic network environment. Accordingly, it proposes a novel classification approach which is a combination of multi-classifier. This method combines the features of a range of classifiers and then achieves traffic classification by means of majority voting and instance selection. Moreover, comparative experiments show that this method improves the classification accuracy, the generalization performance and the ability to adapt to the dynamic network environment. However, it is worth noting that the method has a larger implement complexity and time complexity than these of single algorithm.