Feature Reduction Method Comparison Towards Explainability and Efficiency in Cybersecurity Intrusion Detection Systems. (arXiv:2303.12891v1 [cs.LG])

In the realm of cybersecurity, intrusion detection systems (IDS) detect and
prevent attacks based on collected computer and network data. In recent
research, IDS models have been constructed using machine learning (ML) and deep
learning (DL) methods such as Random Forest (RF) and deep neural networks
(DNN). Feature selection (FS) can be used to construct faster, more
interpretable, and more accurate models. We look at three different FS
techniques; RF information gain (RF-IG), correlation feature selection using
the Bat Algorithm (CFS-BA), and CFS using the Aquila Optimizer (CFS-AO). Our
results show CFS-BA to be the most efficient of the FS methods, building in 55%
of the time of the best RF-IG model while achieving 99.99% of its accuracy.
This reinforces prior contributions attesting to CFS-BA’s accuracy while
building upon the relationship between subset size, CFS score, and RF-IG score
in final results.