Performance Evaluation of C-Fuzzy Decision Tree Based IDS With Different Distance Measures

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.