A novel fuzzy anomaly detection method based on clonal selection clustering algorithm

Authors:
Fenghua Lang;Jian Li;Yixian Yang
Affiliations:
Information Security Center, State Key Laboratory of Networking and Switching, Beijing University of Posts and Telecommunications, Beijing, P.R. China;Information Security Center, State Key Laboratory of Networking and Switching, Beijing University of Posts and Telecommunications, Beijing, P.R. China;Information Security Center, State Key Laboratory of Networking and Switching, Beijing University of Posts and Telecommunications, Beijing, P.R. China
Venue:
ICMLC'05 Proceedings of the 4th international conference on Advances in Machine Learning and Cybernetics
Year:
2005

Citing 2
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Abstract

This paper presents a novel unsupervised fuzzy clustering method based on clonal selection algorithm for anomaly intrusion detection in order to solve the problem of fuzzy k-means algorithm which is particularly sensitive to initialization and fall easily into local optimization. This method can quickly obtain the global optimal clustering with a clonal operator which combines evolutionary search, global search, stochastic search and local search, then detect abnormal network behavioral patterns with a fuzzy detection algorithm. Simulation results on the data set KDD CUP99 show that this method can efficiently detect unknown intrusions with lower false positive rate and higher detection rate.