Abstract
Intrusion Detection Systems detect the malicious attacks which generally include theft of information or data. It is found from the studies that clustering based intrusion detection methods may be helpful in detecting unknown attack patterns compared to traditional intrusion detection systems. In this paper a new clustering algorithm is proposed to work on network intrusion data. The algorithm is experimented with KDD99 dataset and found satisfactory results.
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
Sabahi, F., Movaghar, A.: Intrusion Detection: A Survey. In: The Proceedings of 3rd International Conference on Systems and Networks Communications, ICSNC 2008. IEEE (2008) ISBN: 978-0-7695-3371-1
Han, J., Kamber, M.: Data Mining Concepts and Techniques, 2nd edn. Elsevier ISBN: 81-312-0535-5
Kaufman, L., Rousseeuw, P.J.: Finding Groups in Data: An introduction to Cluster analysis. John Wiley, New York (1990) ISBN 0-471-85233-3
McQueen, J.B.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, pp. 281–297. Univ. of California Press, Berkeley (1967)
Samarjeet Borah, Ghose, M.K.: Automatic Initialization of Means (AIM): A Proposed Extension to the K-means Algorithm. International Journal of Information Technology & Knowledge Management 3(2), 247–250 (2010) ISSN: 0973-4414
Dataset is, http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html
Guan, Y., Ghorbani, A., Belacel, N.: Y-means: A Clustering Method for Intrusion Detection. In: Proceedings of Canadian Conference on Electrical and Computer Engineering, Montreal, Quebec, Canada, May 4-7, pp. 1083–1086 (2003)
Portnoy, L., Eskin, E., Stolfo, S.: Intrusion Detection with Unlabeled Data Using Clustering. In: Proceedings of the ACM CSS Workshop on Data Mining Applied to Security (DMSA 2001), Philadelphia, PA, November 5-8 (2001)
Yan, K.Q., Wang, S.C., Liu, C.W.: A Hybrid Intrusion Detection System of Cluster-based Wireless Sensor Networks. In: Proceedings of the International Multi-Conference of Engineers and Computer Scientists, IMECS 2009, Hong Kong, March 18 - 20, vol. I (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Borah, S., Chetry, S.P.K., Singh, P.K. (2011). Hashed-K-Means: A Proposed Intrusion Detection Algorithm. In: Das, V.V., Thankachan, N. (eds) Computational Intelligence and Information Technology. CIIT 2011. Communications in Computer and Information Science, vol 250. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25734-6_153
Download citation
DOI: https://doi.org/10.1007/978-3-642-25734-6_153
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25733-9
Online ISBN: 978-3-642-25734-6
eBook Packages: Computer ScienceComputer Science (R0)