  One of the key challenges of machine learning (ML) based intrusion detection
system (IDS) is the expensive computational complexity which is largely due to
redundant, incomplete, and irrelevant features contain in the IDS datasets. To
overcome such challenge and ensure building an efficient and more accurate IDS
models, many researchers utilize preprocessing techniques such as normalization
and feature selection in a hybrid modeling approach. In this work, we propose a
hybrid IDS modeling approach with an algorithm for feature selection (FS) and
another for building an IDS. The FS algorithm is a wrapper-based with a
decision tree as the feature evaluator. The propose FS method is used in
combination with some selected ML algorithms to build IDS models using the
UNSW-NB15 dataset. Some IDS models are built as a baseline in a single modeling
approach using the full features of the dataset. We evaluate the effectiveness
of our propose method by comparing it with the baseline models and also with
state-of-the-art works. Our method achieves the best DR of 97.95% and shown to
be quite effective in comparison to state-of-the-art works. We, therefore,
recommend its usage especially in IDS modeling with the UNSW-NB15 dataset.
