vioft2nntf2t|tblJournal|Abstract_paper|0xf4ff9d1809000000c46c010001000500 With the ever-increasing growth of computer networks and emergence of electronic commerce in recent years, computer security has become a priority. Intrusion detection system (IDS) is often used as another wall of protection in addition to intrusion prevention techniques. This paper introduces a concept and design of decision trees based on Fuzzy clustering. Fuzzy clustering is the core functional part of the overall decision tree development and the developed tree will be referred to as C-fuzzy decision trees. Distance measure plays an important role in clustering data points. Choosing the right distance measure for a given dataset is a non-trivial problem. In this paper, we study the performance of C-fuzzy decision tree based IDS with different distance measures. We analyzed the results of our study using KDD Cup 1999 data and compared the accuracy of the classifier with different distance measures.
Vinayak Mantoor1, Krishnamoorthy Makkittaya2 and C.B. Chandrakala3
1,2Department of Master of Computer Applications, Manipal Institute of Technology, Karnataka, India,3Department of Information and Communication Technology, Manipal Institute of Technology, Karnataka, India
Decision Trees, Fuzzy Clustering, Experimental Study, Distance Measures, IDS
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| Published By :
ICTACT
Published In :
ICTACT Journal on Soft Computing
( Volume: 2 , Issue: 2 , Pages: 276-279 )
Date of Publication :
January 2012
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147
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