Abstract
MOVICAB-IDS has been previously proposed as a hybrid intelligent Intrusion Detection System (IDS). This on-going research aims to be one step towards adding automatic response to this visualization-based IDS by means of clustering techniques. As a sample case of study for the proposed clustering extension, it has been applied to the identification of different network scans. The aim is checking whether clustering and projection techniques could be compatible and consequently applied to a continuous network flow for intrusion detection. A comprehensive experimental study has been carried out on previously generated real-life data sets. Empirical results suggest that projection and clustering techniques could work in unison to enhance MOVICAB-IDS.
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
Computer Security Threat Monitoring and Surveillance. Technical Report. James P. Anderson Co. (1980)
Denning, D.E.: An Intrusion-Detection Model. IEEE Transactions on Software Engineering 13, 222–232 (1987)
Chih-Fong, T., Yu-Feng, H., Chia-Ying, L., Wei-Yang, L.: Intrusion Detection by Machine Learning: A Review. Expert Systems with Applications 36, 11994–12000 (2009)
Herrero, Á., Corchado, E.: Mining Network Traffic Data for Attacks through MOVICAB-IDS. In: Abraham, A., Hassanien, A.-E., de Carvalho, A.P. (eds.) Foundations of Computational Intelligence Volume 4. SCI, vol. 204, pp. 377–394. Springer, Heidelberg (2009)
Corchado, E., Herrero, Á.: Neural Visualization of Network Traffic Data for Intrusion Detection. Applied Soft Computing 11, 2042–2056 (2011)
Abdullah, K., Lee, C., Conti, G., Copeland, J.A.: Visualizing Network Data for Intrusion Detection. In: Sixth Annual IEEE Information Assurance Workshop - Systems, Man and Cybernetics, pp. 100–108 (2005)
Corchado, E., Fyfe, C.: Connectionist Techniques for the Identification and Suppression of Interfering Underlying Factors. International Journal of Pattern Recognition and Artificial Intelligence 17, 1447–1466 (2003)
Friedman, J.H., Tukey, J.W.: A Projection Pursuit Algorithm for Exploratory Data-Analysis. IEEE Transactions on Computers 23, 881–890 (1974)
Corchado, E., Corchado, J.M., Sáiz, L., Lara, A.M.: Constructing a Global and Integral Model of Business Management Using a CBR System. In: Luo, Y. (ed.) CDVE 2004. LNCS, vol. 3190, pp. 141–147. Springer, Heidelberg (2004)
Fyfe, C., Corchado, E.: Maximum Likelihood Hebbian Rules. In: 10th European Symposium on Artificial Neural Networks (ESANN 2002), pp. 143–148 (2002)
Corchado, E., Han, Y., Fyfe, C.: Structuring Global Responses of Local Filters Using Lateral Connections. Journal of Experimental & Theoretical Artificial Intelligence 15, 473–487 (2003)
Seung, H.S., Socci, N.D., Lee, D.: The Rectified Gaussian Distribution. In: Advances in Neural Information Processing Systems, vol. 10, pp. 350–356 (1998)
Jain, A.K., Murthy, M.N., Flynn, P.J.: Data Clustering: A Review. ACM Computing Surveys 31 (1999)
Anderberg, M.R.: Cluster Analysis for Applications. Academic Press, Inc., New York (1973)
Jain, A.K., Dubles, R.C.: Algorithms for Clustering Data. Prentice-Hall Advanced Reference Series. Prentice-Hall, Inc., Upper Saddle River (1988)
Diday, E., Simon, J.C.: Clustering Analysis. In: Fu, K.S. (ed.) Digital Pattern Recognition, pp. 47–94. Springer, Secaucus (1976)
Michalski, R., Stepp, R.E., Diday, E.: Automated construction of classifications: conceptual clustering versus numerical taxonomy. IEEE Trans. Pattern Anal. Mach. Intell. PAMI-5(5), 396–409 (1983)
Mao, J., Jones, A.K.: A self-organizing network for hyperellipsoidal clustering (HEC). IEEE Trans. Neural Netw. 7, 16–29 (1996)
McQueen, J.: Some methods for classification and analysis of multivariate observacions. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, pp. 281–297 (1967)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sánchez, R., Herrero, Á., Corchado, E. (2013). Clustering for Intrusion Detection: Network Scans as a Case of Study. In: Herrero, Á., et al. International Joint Conference CISIS’12-ICEUTE´12-SOCO´12 Special Sessions. Advances in Intelligent Systems and Computing, vol 189. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33018-6_4
Download citation
DOI: https://doi.org/10.1007/978-3-642-33018-6_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33017-9
Online ISBN: 978-3-642-33018-6
eBook Packages: EngineeringEngineering (R0)