Abstract
Intrusion Detection based on Adaptive Resonance Theory and Artificial Immune Network Clustering (ID-ARTAINC) is proposed in this paper. First the mass data for intrusion detection are pretreated by Adaptive Resonance Theory (ART) network to form glancing description of the data and to get vaccine. The outputs of ART network are considered as initial antibodies to train an Immune Network, Last Minimal Spanning Tree is employed to perform clustering analysis and obtain characterization of normal data and abnormal data. ID-ARTAINC can deal with mass unlabeled data to distinguish between normal and anomaly and to detect unknown attacks. The computer simulations on the KDD CUP99 dataset show that ID-ARTAINC achieves higher detection rate and lower false positive rate.
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References
Lee, W., Stolfo, S.J., Mok, K.: Data Mining Work Flow Environments Experiences in Intrusion Detection. In: Proceedings of the 1999 Conference on Knowledge Discovery and Data Mining (1999)
Luo, M., Wwang, L.N.: An Unsupervised Clustering-Based Intrusion Detection Method. Acta Eelctronica Sinica 30, 1713–1716 (2003)
Portnoy, L.: Intrusion Detection with Unlabeled Data using Clustering. Undergraduate Thesis. Columbia University (2000)
de Castro, L.N., Von Zuben, F.J.: An Evolutionary Immune Network for Data Clustering. In: Proc. of IEEE SBRN. Rio de Janeiro, pp. 84–89 (2000)
de Castro, L.N., Timmis, J.: Hierarchy and Convergence of Immune Networks: Basic Ideas and Preliminary Results. In: International Conference on Artificial Immune Systems, Canterbury, Kent (2002)
Jiao, L.C.: Neural Network System Theory, vol. 12. Xidian University Press (1990)
Everitt, B.: Cluster Analysis. Heinemann Educational Books Ltd. (1974)
kdd cup 1999 dataset, (1999), http://kdd.ics.uci.edu/databases/kdd_cup99/kdd_cup99.html
Catlett, J.: On Changing Continuous Attributes into Ordered Discrete Attributes. In: Proceedings of the European working session on learning on Machine learning, pp. 164–178 (1991)
Han, J.W.: Data Mining Concepts and Techniques. China Machine Press (2001)
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© 2005 Springer-Verlag Berlin Heidelberg
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Liu, F., Bai, L., Jiao, L. (2005). Intrusion Detection Based on ART and Artificial Immune Network Clustering. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3611. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539117_109
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DOI: https://doi.org/10.1007/11539117_109
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28325-6
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