Feature selection and intrusion detection using hybrid flexible neural tree

Abstract

Current Intrusion Detection Systems (IDS) examine all data features to detect intrusion or misuse patterns. Some of the features may be redundant or contribute little (if anything) to the detection process. The purpose of this study is to identify important input features in building an IDS that is computationally efficient and effective. This paper proposes an IDS model based on general and enhanced Flexible Neural Tree (FNT). Based on the pre-defined instruction/operator sets, a flexible neural tree model can be created and evolved. This framework allows input variables selection, over-layer connections and different activation functions for the various nodes involved. The FNT structure is developed using an evolutionary algorithm and the parameters are optimized by particle swarm optimization algorithm. Empirical results indicate that the proposed method is efficient. © Springer-Verlag Berlin Heidelberg 2005.