Genetic Programming Feature Extraction with Different Robust Classifiers for Network Intrusion Detection

In this paper, we compare the performance of three traditional robust classifiers (Neural Networks, Support Vector Machines, and Decision Trees) with and without utilizing multi-objective genetic programming in the feature extraction phase. This work argues that effective feature extraction can significantly enhance the performance of these classifiers. We have applied these three classifiers stand alone to real world five datasets from the UCI machine learning database and also to network intrusion “KDD-99 cup” dataset. Then, the experiments were repeated by adding the feature extraction phase. The results of the two approaches are compared and conclude that the effective method is to evolve optimal feature extractors that transform input pattern space into a decision space in which the performance of traditional robust classifiers can be enhanced. General Terms Pattern Recognition, Classification, Network Intrusion.

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