Kappa-fuzzy ARTMAP: A feature selection based methodology to intrusion detection in computer networks

Abstract

Intrusions in computer networks have driven the development of various techniques for intrusion detection systems (IDSs). In general, the existing approaches seek two goals: high detection rate and low false alarm rate. The problem with such proposed solutions is that they are usually processing intensive due to the large size of the training set in place. We propose a technique that combines a fuzzy ARTMAP neural network with the well-known Kappa coefficient to perform feature selection. By adding the Kappa coefficient to the feature selection process, we managed to reduce the training set substantially. The evaluation results show that our proposal is capable of detecting intrusions with high accuracy rates while keeping the computational cost low. © 2013 IEEE.