Identifying important features for intrusion detection using discriminant analysis and support vector machine

A lightweight network intrusion detection system is more efficient and effective for the real world requirement. Higher performance may result if the insignificant and/or useless features can be eliminated. Discriminant Analysis can identify the significance of the examined features. In this paper Discriminant Analysis and Support Vector Machine are combined to detect network intrusion. Empirical results indicate that using the important feature set extracted from the Discriminant Analysis can get, nearly the same performance as the full feature set. A comparative study of using different feature selection methods is also shown to prove the usefulness of our approach.