Feature deduction and ensemble design of intrusion detection systems

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. We investigated the performance of two feature selection algorithms involving Bayesian networks (BN) and Classification and Regression Trees (CART) and an ensemble of BN and CART. Empirical results indicate that significant input feature selection is important to design an IDS that is lightweight, efficient and effective for real world detection systems. Finally, we propose an hybrid architecture for combining different feature selection algorithms for real world intrusion detection.