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Abstract: Page-based Linear Genetic Programming (GP) is proposed and implemented with two-layer Subset Selection to address a two-class intrusion detection classification problem as defined by the KDD-99 benchmark dataset. By careful adjustment of the relationship between subset layers, over fitting by individuals to specific subsets is avoided. Moreover, efficient training on a dataset of 500,000 patterns is demonstrated. Unlike the current approaches to this benchmark, the learning algorithm is... (Update)
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BibTeX entry: (Update)
@misc{ heidelberg-cantpaz,
author = "Springer-Verlag Berlin Heidelberg",
title = "E. Cant-Paz et al. (Eds.): GECCO 2003, LNCS 2724, pp. 2325--2336, 2003.",
url = "citeseer.ist.psu.edu/760630.html" }
Citations (may not include all citations):70 A data mining framework for building intrusion detection mod.. - Wenke, Stolfo et al. - 1999 DBLP
19 Dynamic Training Subset Selection for Supervised Learning in.. - Gathercole, Ross - 1994
3 Evaluating Intrusion Detection Systems: the 1998 DARPA Off-L.. - Lippmann, Fried et al. - 2000
2 and 1999 DARPA Intrusion Detection System Evaluations as Per.. (context) - Testing, Systems et al. - 2000
1 The MP13 Approach to the KDD'99 Classifier Learning Contest (context) - Vladimir, Alexei et al. - 2000 ACM DBLP
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