Earticle

초록

영어

Intrusion detection data often have some characteristics such as nonlinearity, higher dimension, much redundancy and noise, and partial continuous-attribute. This paper presents a new ensemble algorithm to improve intrusion detection precision. Firstly, it generates multiple training subsets in difference by using bootstrap technology. Then using neighborhood rough sets with different radiuses to make attribute reduction in these subsets, obtained the training subsets with greater difference, while Particle Swarm Optimization is used to optimize parameters of support vector machine in order to get base classifiers with greater difference and higher precision. Finally, the above base classifiers were integrdinedd by weighted synthesis method. The result of the emulation experiment in KDD99 data set indicates that this algorithm can effectively improve intrusion detection precision ,and it has higher generalization and stability.

목차

Abstract
 1. Introduction
 2. Ensemble Algorithm
  2.1. Attribute Reduction based on Neighborhood Rough Set
  2.2. Parameter Selection of SVM based on PSO
  2.3. Idea and Framework of this Algorithm
 3. Emulation Experiment
  3.1. Experiment Data
  3.2. Standard of Evaluating Algorithm
  3.3. Experiment Methods
  3.4. Result and Analysis of the Experiment
 4. Conclusions
 Acknowledgements
 References

키워드

저자

• Hui Zhao [ School of Mathematics and Computer Science, Shaanxi University of Technology ]

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