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Fuzzy Belief Reasoning for Intrusion Detection Design | IEEE Conference Publication | IEEE Xplore

Fuzzy Belief Reasoning for Intrusion Detection Design


Abstract:

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 (...Show More

Abstract:

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.
Date of Conference: 26-28 November 2007
Date Added to IEEE Xplore: 25 February 2008
Print ISBN:978-0-7695-2994-3
Conference Location: Kaohsiung, Taiwan

References

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