An Internet Traffic Identification Approach Based on GA and PSO-SVM

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, the authors propose an approach to classify both TCP and UDP traffic flows using the Support Vector Machine (SVM) algorithm. In this approach, they select the best feature subset using Genetic Algorithm, and then they calculate the correspondence weight of each feature selected by Particle Swarm Optimization (PSO). In addition, the traditional SVM algorithm is optimized by PSO algorithm.