Earticle

초록

영어

This paper aimed at the actual situation of the difficult of getting a lot of the training sample of the security computer network system in the distributed intrusion detection. In this paper, we studied how to increase the intrusion detection accuracy in the case of small samples, so that processing, maintenance and deal with the invasion of the network timely. In this paper, we proposed a new intrusion detection method based on improved SVM Co - training. The specific implementation process of the algorithm is presented. Through the simulation experiments based on the actual data showed that the method is effective. Apply this method to a classified computer network system, effectively realized the detection to outside intruders and internal intruder.

목차

Abstract
 1. Introduction
 2. Some Definitions on Issues
 3. Semi-supervised Learning Algorithm based on Improved SVM
 4. The Actual Data Simulation
  4.1. The Algorithm Pseudo Code
  4.2. Parameter Setting
  4.3. Simulation Results
 5. Classified Computer Network System based on Distributed Intrusion Detection Technology
 6. Conclusion
 Acknowledgements
 References

키워드

저자

• Xin Huang [ Guangxi Agricultural-vocational Technique College, Nanning Guangxi 530007, China ]
• Rongze Wan [ Guangxi Agricultural-vocational Technique College, Nanning Guangxi 530007, 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