Fuzzy Belief Reasoning for Intrusion Detection Design
Te‐Shun Chou, Kang K. Yen, Niki Pissinou, Kia Makki · 2007
In this paper, we propose a method to resolve uncertainty problems by incorporating fuzzy clustering technique and Dempster-Shafer theory. Also, the k-nearest neighbors (k-NN) technique is applied to speed up the detection process and C4.5 decision tree algorithm is used to improve the classification accuracy. For verifying the performance of our classifier, DARPA KDD99 intrusion detection evaluation data set is used. We compare the results of our proposed approach with those of k-NN classifier, fuzzy k-NN classifier and evidence-theoretic k-NN classifier. The result indicates that our approach has a better performance than these from the other three classifiers.