Abstract
Computer security or network security has become one of the biggest issues now-a-days. Intrusion Detection process detects malicious attacks which generally includes theft of information or data. Traditional IDS (Intrusion Detection System) detects only those attacks which are known to them. But they rarely detect unknown intrusions. Clustering based method may be helpful in detecting unknown attack patterns. In this paper an attempt has been made to propose a new intrusion detection method based on clustering. The algorithm is experimented with KDD99 dataset and is found to produce 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, Los Alamitos (2008)
Berkhin, P.: Survey of clustering data mining techniques. Technical report, Accrue Software, San Jose, CA (2002)
Terry Brugger, S.: Data Mining Methods for Network Intrusion Detection. University of California, Davis (2004)
Prerau, M.J., Eskin, E.: Unsupervised anomaly detection using an optimized K-nearest neighbors algorithm. Master”s thesis, http://www.music.columbia.edu/~mike/publications/thesis.pdf
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 (2003)
Bloedorn, E., Christiansen, A.D., Hill, W., Skorupka, C., Talbot, L.M., Tivel, J.: Data mining for network intrusion detection: How to get started (August 2001), http://citeseer.nj.nec.com/523955.html
Lee, W., Stolfo, S.J.: Data Mining Approaches for Intrusion Detection. In: Proceedings of the 1998 USENIX Security Symposium (1998)
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 MultiConference of Engineers and Computer Scientists 2009, IMECS 2009, Hong Kong, March 18-20, vol. I (2009)
Zhong, S., Khoshgoftaar, T.M., Seliya, N.: Clustering-based network intrusion detection (2007)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification (2001)
Zanero, S., Savaresi, S.M.: Unsupervised learning techniques for a intrusion detection system (2004)
Lazarevic, A., Ertoz, L., Kumar, V., Ozgur, A., Srivastava, J.: A Comparative study of anomaly detection schemes in network intrusion detection (2003)
Zhang, R., et al.: BIRCH: An efficient data clustering method for very large databases (1996)
Lee, S.: Data mining approaches for intrusion detection (1998)
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., Chakravorty, D., Chawhan, C., Saha, A. (2011). Advanced Clustering Based Intrusion Detection (ACID) Algorithm. In: Abraham, A., Mauri, J.L., Buford, J.F., Suzuki, J., Thampi, S.M. (eds) Advances in Computing and Communications. ACC 2011. Communications in Computer and Information Science, vol 192. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22720-2_4
Download citation
DOI: https://doi.org/10.1007/978-3-642-22720-2_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-22719-6
Online ISBN: 978-3-642-22720-2
eBook Packages: Computer ScienceComputer Science (R0)