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.
This work was supported in part by Spanish Ministerio de Ciencia e Innovación under Project Code TIN 2006-02402, partially supported by the European Union ERDF, and by Xunta de Galicia under Project Code 08TIC012105PR.
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Porto-Díaz, I., Martínez-Rego, D., Alonso-Betanzos, A., Fontenla-Romero, O. (2009). Combining Feature Selection and Local Modelling in the KDD Cup 99 Dataset. In: Alippi, C., Polycarpou, M., Panayiotou, C., Ellinas, G. (eds) Artificial Neural Networks – ICANN 2009. ICANN 2009. Lecture Notes in Computer Science, vol 5768. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04274-4_85
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