# Feature Reduction Method Comparison Towards Explainability and Efficiency in Cybersecurity Intrusion Detection Systems

arXiv: https://arxiv.org/abs/2303.12891 · HTML: https://mlsanity.com/paper/2303.12891/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 22 references that are in the map.
- First posted on arXiv 22 Mar 2023.

## Builds on (most cited first, 1 of 1)

- [Bat Algorithm: Literature Review and Applications](https://mlsanity.com/paper/1308.3900/) (2013)

## Built on by (most cited first, 1 of 1)

- [Enhancing Efficiency and Privacy in Memory-Based Malware Classification through Feature Selection](https://mlsanity.com/paper/2310.00516/) (2023)

## Abstract (arXiv)

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.