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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Dasgupta, D., Gonzalez, F.: An Immunity-Based Technique to Characterize Intrusions in Computer Networks. IEEE Trans. Evol. Comput. 6, 281–291 (2002)
Sugbae, C., Sangjun, H.: Two Sophisticated Techniques to Improved HMM-Based Intrusion Detection Systems. In: Proceeding of RAID, Pittsburgh, September 2003, pp. 207–219 (2003)
He, D., Leung, H.: CFAR Intrusion Detection Method Based on Support Vector Machine Prediction. In: Proceeding of CIMSA 2004, Boston, July 2004, pp. 10–15 (2004)
Jeme, N.K.: Towards a Network Theory of the Immune System. Ann. Immunol., 373–389 (January 1974)
Castro, L.N., Von Zuben, F.J.: Learning and Optimization Using the Clone Selection Principal. IEEE Trans. Evol. Comput. 6(3), 239–251 (2002)
Jie, L., Xinbo, G., Licheng, J.: A Novel Clustering Method with Network Structure Based on Clonal Algorithm. In: Proceedings of ICASSP 2004, Canada, May 2004, pp. 793–796 (2004)
Portnoy, L., Eskin, E., Stolfo, S.: Intrusion Detection with Unlabeled Data Using Clustering. In: Proceedings of DMSA 2001, Philadelphia, November 2001, pp. 5–8 (2001)
KDD CUP 1999 Data Set (1999), http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
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
Download citation
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
eBook Packages: Computer ScienceComputer Science (R0)