Network Traffic Classification using Genetic Algorithms based on Support Vector Machine
Jie Cao, Zhiyi Fang · International Journal of Security and Its Applications · 2016
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.