一种网络流量分类特征的产生及选择方法

在基于机器学习方法的网络流量分类系统中,特征产生及特征选择将直接影响到分类的速度及精度。针对这种情况,在特征产生方面,结合Packet-Level和Flow-Level上的信息,对报文属性(大小、个数、时间、标志位)、流的属性(时间)进行分析和研究,产生了37个网络流量统计特征。在特征选择方面,提出一种结合Filter模型和Wrapper模型的网络流量特征选择方法,实现了降维作用。实验表明,提出的方法提高了分类的精度。

Method of feature generation and selection for network traffic classification

In the System of Network Traffic Classification based on machine learning method, feature generation and feature selection directly affects the speed and accuracy of classification. To solve this problem, in feature generation aspect, we analyze the packet’s attributes (size, count, time, flag) and flow’s attributes (time) from the information of Packet-Level and Flow Level, and 37 statistical features are generated. In feature selection aspect, we proposes a method of feature selection integrating Filter model and Wrapper model, to decrease the dimension of features. Experiments show the proposed methods improve the accuracy of classification.