Network traffic classification based on GA-CFS and AdaBoost algorithm
基于GA-CFS和AdaBoost算法的网络流量分类

The selection of feature attribute plays an important role in the network traffic classification. This paper applied a method considering the CFS algorithm as the fitness function of the improved genetic algorithm GA-CFS in order to extract the main flow statistical attributes in the space of 249 attributes and selected 18 attributes of a flow as the best feature subset. Finally it used the AdaBoost algorithm to enhance a series of weak classifiers to the strong classifiers. At the same time, it fulfilled the classification of the network traffic, and further studied the network traffic intensively. The experimental results indicate that GA-CFS and AdaBoost algorithm can achieve higher classification precision compared with the weak classifiers.