In this paper, I present a cost-based approach utilizing decision tree model and neural networks for fast intrusion detection.
Both computational cost and time cost are considered, and different models are trained with respect to the types of protocols and services.
Empirical experiments are carried out on off-line benchmark dataset NSL-KDD.