Earticle

초록

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.

목차

Abstract
 1. Introduction
 2. SVM Model
 3. SVM Parameters Optimization based on GA
 4. Evaluation based on SVM
  4.1 Data Set
  4.2 Pretreatment
  4.3 The Simulation Experiments and Analysis
 5. Conclusion
 References

키워드

저자

• Jie Cao [ College of Computer Science and Technology Jilin University, Changchun, 130012, P.R. China, College of Information Engineering Northeast Dianli University, Jilin, 132012, P.R. China ] Corresponding author
• Zhiyi Fang [ College of Computer Science and Technology Jilin University, Changchun, 130012, P.R. China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Security and Its Applications

• 간기

  격월간

• pISSN

  1738-9976

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605