원문정보
보안공학연구지원센터(IJDTA)
International Journal of Database Theory and Application
Vol.8 No.1
2015.02
pp.149-156
피인용수 : 0건 (자료제공 : 네이버학술정보)
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
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
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