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.
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© 2006 Springer-Verlag Berlin Heidelberg
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Lang, F., Li, J., Yang, Y. (2006). A Novel Fuzzy Anomaly Detection Method Based on Clonal Selection Clustering Algorithm. In: Yeung, D.S., Liu, ZQ., Wang, XZ., Yan, H. (eds) Advances in Machine Learning and Cybernetics. Lecture Notes in Computer Science(), vol 3930. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11739685_67
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DOI: https://doi.org/10.1007/11739685_67
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33584-9
Online ISBN: 978-3-540-33585-6
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