Abstract
The self-organizing map (SOM) have shown to be successful for the analysis of high-dimensional input data as in data mining applications such as network security. However, the static architecture and the lack of representation of hierarchical relations are its main drawbacks. The growing hierarchical SOM (GHSOM) address these limitations of the SOM. The GHSOM is an artificial neural network model with hierarchical architecture composed of independent growing SOMs. One limitation of these neural networks is that they just take into account numerical data, even though symbolic data can be present in many real life problems. In this paper a new GHSOM model with a new metric incorporing both numerical and symbolic data is proposed. This new GHSOM model is proposed for detecting network intrusions. 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 statical models using metrics derived from observation of the user’s actions. Randomly selected subsets that contains both attacks and normal records from the KDD Cup 1999 benchmark are used for training the proposed GHSOM. Experimental results are provided and compared to other hierarchical neural networks.
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References
Kohonen, T.: Self-organized formation of topologically correct feature maps. Biological cybernetics 43(1), 59–69 (1982)
Rauber, A., Merkl, D., Dittenbach, M.: The growing hierarchical self-organizing map: Exploratory analysis of high-dimensional data. IEEE Transactions on Neural Networks 13(6), 1331–1341 (2002)
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 and 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)
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)
Stolfo, S., Fan, W., Lee, W., Prodromidis, A., Chan, P.: Cost-based modeling for fraud and intrusion detection: results from the jam project. In: DARPA Information Survivability Conference and Exposition, 2000, DISCEX 2000. Proceedings, vol. 2, pp. 130–144 (2000)
Sarasamma, S., Zhu, Q., Huff, 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)
Fritzke, B.: Growing grid - a self-organizing network with constant neighborhood range and adaptation strength. Neural Processing Letters 2(5), 9–13 (1995)
Dittenbach, M., Rauber, A., Merkl, D.: Recent advances with the growing hierarchical self-organizing map. In: 3rd Workshop on Self-Organising Maps (WSOM), pp. 140–145 (2001)
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Palomo, E.J., Domínguez, E., Luque, R.M., Muñoz, J. (2008). A New GHSOM Model Applied to Network Security. In: Kůrková, V., Neruda, R., Koutník, J. (eds) Artificial Neural Networks - ICANN 2008. ICANN 2008. Lecture Notes in Computer Science, vol 5163. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87536-9_70
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DOI: https://doi.org/10.1007/978-3-540-87536-9_70
Publisher Name: Springer, Berlin, Heidelberg
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