Earticle

초록

영어

In the present era as internet is growing with exponential pace, computer security has become a critical issue. In recent times data mining and machine learning have been researched extensively for intrusion detection with the aim of improving the accuracy of detection classifier. KDD CUP’ 99 Data set is the most widely used dataset in research domain. Selecting important feature on the basis of rough set based feature selection approach have lead to a simplification of the problem, faster and more accurate detection rates. In this paper, we presented an efficient approach for detecting relevant features from the KDD CUP’99 Data set.

목차

Abstract
 1. Introduction
 2. Basic Concept of Rough Set Theory
  2.1. Information System
  2.2. Indiscrenibility Relation
  2.3. Lower and Upper Approximations
  2.4. Accuracy of Approximation
  2.5. Core and Reduct of Attributes
 3. KDD CUP 99 Data Set
  3.1. Denial-of-Service (DoS)
  3.2. Probing or Surveillance
  3.3. User-to-Root (U2R)
  3.4. Remote-to-Local (R2L)
 4. Proposed Approach
 5. Experimental Analysis and Results
 6. Conclusion and Future Work
 Reference

키워드

저자

• Vinod Rampure [ Department of CSE & IT, Madhav Institute of Technology and Science, Gwalior (M.P), India ]
• Akhilesh Tiwari [ Department of CSE & IT, Madhav Institute of Technology and Science, Gwalior (M.P), India ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Database Theory and Application

• 간기

  격월간

• pISSN

  2005-4270

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605