Abstract
Traditional immune intrusion detection algorithms need lots of labeled training data. However, it is difficult to obtain sufficient labeled data in real situation. In this paper we present a semi-supervised clustering based immune intrusion detection algorithm called SCIID, which can improve the quality of antibodies constantly and enhance the detection rate. Experimental results show that SCIID can get the classes of most unlabeled data in the case of only having a few labeled data, and it can also discover new types of attacks. The detection rate of SCIID is higher than that of simply immune-based approach with the same number of training data.
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
Junqwon, K., Bentley, P.J.: Towards an Artificial Immune System for Network Intrusion Detection: an Investigation of Clonal Selection with a Negative selection Operator. In: Proceedings of the Congress on Evolutionary Computation (CEC-2001), pp. 1244–1252. IEEE Press, Seoul (2001)
Dipankar, D., Fabio, G.: An immunity-based technique to characterize intrusions in computer networks. IEEE Transactions on Evolutionary Computation 6(3), 1081–1088 (2002)
Li, K.-L., Cao, Z., et al.: Some Developments on Semi-Supervised Clustering. Pattern Recognition and Artificial Intelligence 22(5), 735–742 (2009) (in Chinese)
Zhong, S.: Semi-supervised model based document Clustering: A comparative study. Machine Learning 65(1), 3–29 (2006)
Wagstaff, K., Cardie, C., Rogers, S., et al.: Constrained K-means Clustering with Background Knowledge. In: Proceedings of International Conference on Machine Learning, San Francisco, USA (2001)
Chapelle, O., Scholkopf, B., Zien, A.: Semi-supervised learning. MIT Press, Mass (2006)
Zhou, Z.H., Li, M.: Semi-Supervised Regression with Co-Training Style Algorithms. IEEE Transactions on Knowledge and Data Engineering 19(11), 1479–1493 (2007)
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
Wang, X. (2011). Research of Immune Intrusion Detection Algorithm Based on Semi-supervised Clustering. In: Deng, H., Miao, D., Lei, J., Wang, F.L. (eds) Artificial Intelligence and Computational Intelligence. AICI 2011. Lecture Notes in Computer Science(), vol 7003. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23887-1_9
Download citation
DOI: https://doi.org/10.1007/978-3-642-23887-1_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23886-4
Online ISBN: 978-3-642-23887-1
eBook Packages: Computer ScienceComputer Science (R0)