School of Computer Science, Sichuan University, Chengdu, China

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Abstract—Internet traffic identification is currently an important challenge for network management. Many approaches have been proposed to classify different categories of Internet traffic. However, traditional approaches only focus on identifying TCP flows and have ignored the selection of best feature subset for classification. In this paper, we propose an approach to classify both TCP and UDP traffic flows using the Support Vector Machine (SVM) algorithm. In this approach, we select the best feature subset using Genetic Algorithm, and then we calculate the correspondence weight of each feature selected by Particle Swarm Optimization (PSO). In addition, the traditional SVM algorithm is optimized by PSO algorithm. The experimental results demonstrate that this approach can effectively select the feature subset from multiple attributes that can best reflect the differences among different network applications. Moreover, the identification rate is improved by the method of feature weighting and PSO optimized SVM algorithm.

Index Terms—Traffic Identification, Genetic Algorithm, Particle Swarm Optimization, Support Vector Machine, Statistical Characteristics.

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Cite: Jun Tan, Xingshu Chen, Min Du, "An Internet Traffic Identification Approach Based on GA and PSO-SVM," Journal of Computers vol. 7, no. 1, pp. 19-29, 2012.