Network Traffic Classification using Genetic Algorithms based on Support Vector Machine

In recent years, machine learning method has been applied to the extensive research on traffic classification. In these methods, SVM (Support vector machine) is a supervised learning which can improve generalization ability of learning machine effectively. However, the penalty parameter C and kernel function parameter. are generally given by test experience during training of SVM. How to determine the optimal parameters of SVM is a problem to be solved. We proposed a method to deriving the optimal parameters of SVM based on GA (Genetic algorithm). This method does not need to traverse all the parameter points. The method extracts a certain number population from random solutions, and ultimately produces SVM optimal parameters according to the specific rules of operation. Through the method, we derived the optimal parameters combination C and. of SVM. The accuracy of network traffic classification is improved greatly.