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영어

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.