Earticle

초록

영어

This paper describes an ensemble design for cyber security threats detection, which fuses the results from multiple classifiers together to make a final assessment decision. For promoting both speed and accuracy in the detection performance, only some of the features in traffic data are selected for each base classifier. In the kernel of each classifier, we combine Dempster-Shafer theory with k-nearest neighbor technique to solve the uncertainty problems caused by ambiguous and limited intrusion information. In addition, we apply data mining techniques to reduce the number of false alarms. The results indicate that our ensemble approach achieves higher detection rates than that of using a full feature set of classifiers.

목차

Abstract
 1. Introduction
 2. Related Works
 3. Theoretical Framework
  3.1 Ensemble Classifier
  3.2 k-NN Belief Intrusion Detection Algorithm
  3.3 Combination Method
  3.4 Data Mining Classifier
 4. Experimental Methodology
  4.1. The Data Set
  4.2. Preprocessing
  4.3. Data Selection
 5. Experimental Results
 6. Conclusions
 References

키워드

저자

• Te-Shun Chou [ Department of Technology Systems, East Carolina University Greenville ]

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