  Intrusion detection system (IDS) is one of extensively used techniques in a
network topology to safeguard the integrity and availability of sensitive
assets in the protected systems. Although many supervised and unsupervised
learning approaches from the field of machine learning have been used to
increase the efficacy of IDSs, it is still a problem for existing intrusion
detection algorithms to achieve good performance. First, lots of redundant and
irrelevant data in high-dimensional datasets interfere with the classification
process of an IDS. Second, an individual classifier may not perform well in the
detection of each type of attacks. Third, many models are built for stale
datasets, making them less adaptable for novel attacks. Thus, we propose a new
intrusion detection framework in this paper, and this framework is based on the
feature selection and ensemble learning techniques. In the first step, a
heuristic algorithm called CFS-BA is proposed for dimensionality reduction,
which selects the optimal subset based on the correlation between features.
Then, we introduce an ensemble approach that combines C4.5, Random Forest (RF),
and Forest by Penalizing Attributes (Forest PA) algorithms. Finally, voting
technique is used to combine the probability distributions of the base learners
for attack recognition. The experimental results, using NSL-KDD, AWID, and
CIC-IDS2017 datasets, reveal that the proposed CFS-BA-Ensemble method is able
to exhibit better performance than other related and state of the art
approaches under several metrics.
