Earticle

초록

영어

Feature selection is as important for intrusion detection as it is for many other problems. A feature selection algorithm can help system administrators to identify and detect new network attacks efficiently since appropriately chosen features can improve accuracy of intrusion detection
significantly as well as can decrease computational overheads of intrusion detection systems. This paper describes a new proposed feature selection algorithms in detecting intrusions using network audit trails. The proposed method is based on our definition of cluster distance to select good features, and advantages of the proposed feature selection method include independence of data formats (e.g., continuous data or discrete data), suitability for binary classification, and improved intrusion detection accuracy. Experimental results using KDDCup99 datasets show that the proposed model can improve intrusion detection accuracy, compared to other algorithms.

목차

Abstract
 1 Introduction
 2 Related Work
 3 Our Proposed Model: Clustering-based Feature Selection
  3.1 Our Definition of Cluster Distance
  3.2 Feature Selection using Cluster Distances
 4 Experimental Results
 5 Conclusions
 References

키워드

저자

• Jungtaek Seo [ Attached Institute of ETRI ]
• Jungtae Kim [ Secuve Inc. ]
• Jongsub Moon [ Korea University ]
• Boo Jung Kang [ College of Information and Communications Hanyang University ]
• Eul Gyu Im [ College of Information and Communications Hanyang University ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Future Generation Communication and Networking

• 간기

  격월간

• pISSN

  2233-7857

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605