Combining Feature Selection and Local Modelling in the KDD Cup 99 Dataset

Authors:
Iago Porto-Díaz;David Martínez-Rego;Amparo Alonso-Betanzos;Oscar Fontenla-Romero
Affiliations:
Department of Computer Science, University of A Coruña, Spain;Department of Computer Science, University of A Coruña, Spain;Department of Computer Science, University of A Coruña, Spain;Department of Computer Science, University of A Coruña, Spain
Venue:
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
Year:
2009

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Abstract

In this work, a new approach for intrusion detection in computer networks is introduced. Using the KDD Cup 99 dataset as a benchmark, the proposed method consists of a combination between feature selection methods and a novel local classification method. This classification method ---called FVQIT (Frontier Vector Quantization using Information Theory)--- uses a modified clustering algorithm to split up the feature space into several local models, in each of which the classification task is performed independently. The method is applied over the KDD Cup 99 dataset, with the objective of improving performance achieved by previous authors. Experimental results obtained indicate the adequacy of the proposed approach.