Abstract
An intrusion detection system (IDS) monitors the IP packets flowing over the network to capture intrusions or anomalies. One of the techniques used for anomaly detection is building statistical models using metrics derived from observation of the user’s actions. A neural network model based on self organization is proposed for detecting intrusions. The self-organizing map (SOM) has shown to be successful for the analysis of high-dimensional input data as in data mining applications such as network security. The proposed growing hierarchical SOM (GHSOM) addresses the limitations of the SOM related to the static architecture of this model. The GHSOM is an artificial neural network model with hierarchical architecture composed of independent growing SOMs. Randomly selected subsets that contain both attacks and normal records from the KDD Cup 1999 benchmark are used for training the proposed GHSOM.
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.
Similar content being viewed by others
References
Denning, D.: An intrusion-detection model. Software Engineering. IEEE Transactions on SE 13(2), 222–232 (1987)
Lee, W., Stolfo, S., Chan, P., Eskin, E., Fan, W., Miller, M., Hershkop, S., Zhang, J.: Real time data mining-based intrusion detection. In: DARPA Information Survivability Conference & Exposition II, vol. 1, pp. 89–100 (2001)
Maxion, R., Tan, K.: Anomaly detection in embedded systems. IEEE Transactions on Computers 51(2), 108–120 (2002)
Tan, K., Maxion, R.: Determining the operational limits of an anomaly-based intrusion detector. IEEE Journal on Selected Areas in Communications 21(1), 96–110 (2003)
Ying, H., Feng, T.J., Cao, J.K., Ding, X.Q., Zhou, Y.H.: Research on some problems in the kohonen som algorithm. In: International Conference on Machine Learning and Cybernetics, vol. 3, pp. 1279–1282 (2002)
Kohonen, T.: Self-organized formation of topologically correct feature maps. Biological cybernetics 43(1), 59–69 (1982)
Fritzke, B.: Growing grid - a self-organizing network with constant neighborhood range and adaptation strength. Neural Processing Letters 2(5), 9–13 (1995)
Lee, W., Stolfo, S., Mok, K.: A data mining framework for building intrusion detection models. In: IEEE Symposium on Security and Privacy, pp. 120–132 (1999)
Sarasamma, S., Zhu, Q., Hu, J.: Hierarchical kohonenen net for anomaly detection in network security. IEEE Transactions on Systems Man and Cybernetics Part B-Cybernetics 35(2), 302–312 (2005)
DeLooze, L., DeLooze, A.F.: Attack characterization and intrusion detection using an ensemble of self-organizing maps. In: 7th Annual IEEE Information Assurance Workshop, pp. 108–115 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Palomo, E.J., Domínguez, E., Luque, R.M., Muñoz, J. (2009). An Intrusion Detection System Based on Hierarchical Self-Organization. In: Corchado, E., Zunino, R., Gastaldo, P., Herrero, Á. (eds) Proceedings of the International Workshop on Computational Intelligence in Security for Information Systems CISIS’08. Advances in Soft Computing, vol 53. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88181-0_18
Download citation
DOI: https://doi.org/10.1007/978-3-540-88181-0_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-88180-3
Online ISBN: 978-3-540-88181-0
eBook Packages: EngineeringEngineering (R0)