Anomaly-Based Intrusion Detection using Fuzzy Rough Clustering

Authors:
Witcha Chimphlee;Abdul Hanan Abdullah;Mohd Noor Md Sap;Surat Srinoy;Siriporn Chimphlee
Affiliations:
Suan Dusit Rajabhat University;Universiti Teknologi Malaysia;Universiti Teknologi Malaysia;Suan Dusit Rajabhat University;Suan Dusit Rajabhat University
Venue:
ICHIT '06 Proceedings of the 2006 International Conference on Hybrid Information Technology - Volume 01
Year:
2006

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Review: The use of computational intelligence in intrusion detection systems: A review

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Semi-supervised outlier detection based on fuzzy rough C-means clustering

Mathematics and Computers in Simulation
A novel intrusion detection system based on hierarchical clustering and support vector machines

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Mutual information-based feature selection for intrusion detection systems

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Applied Intelligence

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Abstract

It is an important issue for the security of network to detect new intrusion attack and also to increase the detection rates and reduce false positive rates in Intrusion Detection System (IDS). Anomaly intrusion detection focuses on modeling normal behaviors and identifying significant deviations, which could be novel attacks. The normal and the suspicious behavior in computer networks are hard to predict as the boundaries between them cannot be well defined. We apply the idea of the Fuzzy Rough C-means (FRCM) to clustering analysis. FRCM integrates the advantage of fuzzy set theory and rough set theory that the improved algorithm to network intrusion detection. The experimental results on dataset KDDCup99 show that our method outperforms the existing unsupervised intrusion detection methods.