Earticle

초록

영어

Introducing the artificial intelligence learning algorithm to solve the problem of network security is a focus of current research. We introduce the clustering algorithm into artificial intelligence learning algorithm and apply Fuzzy Support Machines to the intrusion detection. We put forward a method which is based on Fuzzy Support Machines. Then, we chose an appropriate RBF kernel function according to the characteristic of intrusion detection. And we get the intrusion detection algorithm based on Fuzzy Support Machines. The algorithm in this paper reduces the training time and improves the efficiency of the algorithm. Experimental results show that this method improves the fuzzy support vector machine training efficiency, and it is also very effective in intrusion detection. The first part of this paper is the introduction of the related problem. The second part is the concept of Fuzzy Support Vector Machine. The third part is the choice of the clustering center. The fourth part is the process of intrusion detection algorithm. The final part is the experiment.

목차

Abstract
 1. Introduction
 2. Fuzzy Support Vector Machine
 3. The Selection of the Efficient Clustering Center Set
 4. Vector Machine Intrusion Detection Algorithm based on Clustering Fuzzy Support
 5. Experiment
  5.1. Numerical Experiments
  5.2. Application in Intrusion Detection
  5.3. Intrusion Detection Experiment
 6. Conclusion
 References

키워드

저자

• Zhai Jinbiao [ State Key Laboratory of Software Development Environment, School of Computer Science and Engineering, BeiHang University ]

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