Abstract
Detecting network intrusions is becoming crucial in computer networks. In this paper, an Intrusion Detection System based on a competitive learning neural network is presented. Most of the related works use the self-organizing map (SOM) to implement an IDS. However, the competitive neural network has less complexity and it is faster than the SOM, achieving similar results. In order to improve these results, we have used a repulsion method among neurons to avoid overlapping. Moreover, we have taken into account the presence of quantitative data in the input data, and they have been pre-processed appropriately to be supplied to the neural network. Therefore, the current metric based on Euclidean distance to compare two vectors can be used. The experimental results were obtained by applying the KDD Cup 1999 benchmark data set, which contains a great variety of simulated networks attacks. Comparison with other related works is provided.
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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)
Cannady, J.: Artificial neural networks for misuse detection. In: Proceedings of the 1998 National Information Systems Security Conference (NISSC 1998), Arlington, VA, October 5-8, 1998, pp. 443–456 (1998)
Kohonen, T.: Self-organized formation of topologically correct feature maps. Biological cybernetics 43(1), 59–69 (1982)
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)
Jain, A., Dubes, R.: Algorithms for clustering data. Prentice-Hall, Inc., Englewood Cliffs (1988)
Lei, J., Ghorbani, A.: Network intrusion detection using an improved competitive learning neural network. In: 2nd Annual Conference on Communication Networks and Services Research, pp. 190–197 (2004)
Depren, O., Topallar, M., Anarim, E., Ciliz, M.: An intelligent intrusion detection system (ids) for anomaly and misuse detection in computer networks. Expert Systems with Applications 29(4), 713–722 (2005)
Bay, S., Kibler, D., Pazzani, M., Smyth, P.: The uci kdd archive of large data sets for data mining research and experimentation. SIGKDD Explor. Newsl. 2(2), 81–85 (2000)
DeLooze, L., DeLooze, L. 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)
Mitrokotsa, A., Douligeris, C.: Detecting denial of service attacks using emergent self-organizing maps. In: 5th IEEE International Symposium on Signal Processing and Information Technology, pp. 375–380 (2005)
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Palomo, E.J., Domínguez, E., Luque, R.M., Muñoz, J. (2008). A Competitive Neural Network for Intrusion Detection Systems. In: Le Thi, H.A., Bouvry, P., Pham Dinh, T. (eds) Modelling, Computation and Optimization in Information Systems and Management Sciences. MCO 2008. Communications in Computer and Information Science, vol 14. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87477-5_56
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DOI: https://doi.org/10.1007/978-3-540-87477-5_56
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