Network traffic classification based on GA-CFS and AdaBoost algorithm

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.