关键词: 流量分类, 统计特征, 误差反向传播, Levenberg-Marquardt算法, 遗传算法

Abstract: In order to solve the problems in current work that rely on the traditional method of traffic classification，such as low accuracy，limited application region，an effective approach for network traffic classification named GA-LM is proposed.This method employs the classification method based on neural network as the classification model of network traffic，and applies L-M algorithm which is an improved BP algorithms and Genetic Algorithm（GA） that optimizes neural network weights，which will speed up the convergence of the neural network and improve the classification performance.The experimental data sets that are collected from actual networks are conducted experiments.The results show that the convergence speed of the method is faster and GA-LM has better feasibility and high accuracy which can be used effectively to the application of network traffic classification.

Key words: traffic classification, statistical features, error back propagation, Levenberg-Marquardt algorithm, genetic algorithm