A novel intrusion detection method based on clonal selection clustering algorithm

This paper presents a novel unsupervised fuzzy clustering method based on clonal selection algorithm for anomaly detection in order to solve the problem of fuzzy k-means algorithm which is much more sensitive to the initialization and easy to fall into local optimization. This method can quickly obt...

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Bibliographic Details
Published in:	2005 International Conference on Machine Learning and Cybernetics Vol. 6; pp. 3905 - 3910 Vol. 6
Main Authors:	Ji-Qing Xian, Feng-Hua Lang, Xian-Lun Tang
Format:	Conference Proceeding
Language:	English
Published:	IEEE 2005
Subjects:	Anomaly detection artificial immune Automation clonal selection algorithm Clustering algorithms Clustering methods Detection algorithms fuzzy clustering fuzzy k-means algorithm Fuzzy sets genetic algorithm Genetic algorithms Intrusion detection Machine learning Stochastic processes Training data
ISBN:	0780390911, 9780780390911
ISSN:	2160-133X
Online Access:	Get full text
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