Abstract
In this paper, we present an efficient and biologically inspired clustering model for anomaly intrusion detection. The proposed model called Ant Colony Clustering Model (ACCM) that improves existing ant-based clustering model in searching for optimal clustering heuristically. Experimental results on KDD-Cup99 benchmark data show that ACCM is effective to detect known and unseen attacks with high detection rate and low false positive rate.
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© 2005 Springer-Verlag Berlin Heidelberg
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Tsang, W., Kwong, S. (2005). Unsupervised Anomaly Intrusion Detection Using Ant Colony Clustering Model. In: Abraham, A., Dote, Y., Furuhashi, T., Köppen, M., Ohuchi, A., Ohsawa, Y. (eds) Soft Computing as Transdisciplinary Science and Technology. Advances in Soft Computing, vol 29. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-32391-0_30
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DOI: https://doi.org/10.1007/3-540-32391-0_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25055-5
Online ISBN: 978-3-540-32391-4
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