Feature Reduction Method Comparison Towards Explainability and Efficiency in Cybersecurity Intrusion Detection Systems
This work addresses efficiency and explainability in cybersecurity intrusion detection, but it is incremental as it builds on prior contributions.
The paper compared three feature selection methods for intrusion detection systems, finding that CFS-BA built models in 55% of the time of the best RF-IG model while achieving 99.99% of its accuracy.
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.