Feature selection plays a vital role in building machine learning models. Irrelevant features in data affect the accuracy of the modeland increase the training time needed to build the model. Feature selection is an important process to build Intrusion DetectionSystem (IDS). In this paper, a wrapper feature selection algorithm for IDS is proposed. This algorithm uses the pigeon inspiredoptimizer to utilize the selection process. A new method to binarize a continuous pigeon inspired optimizer is proposed andcompared to the traditional way for binarizing continuous swarm intelligent algorithms. The proposed algorithm was evaluatedusing three popular datasets:K DDCU P99, NLS-KDD and UNSW-NB15. The proposed algorithm outperformed several featureselection algorithms from state-of-the-art related works in terms of TPR, FPR, accuracy, and F-score. Also, the proposed cosinesimilarity method for binarizing the algorithm has a faster convergence than the sigmoid method.
Feature Selection, Intrusion Detection System, KDDCUP, Pigeon Inspired Optimizer.
https://doi.org/10.1016/j.eswa.2020.113249
Alazzam, H., Sharieh, A., & Sabri, K. E. (2020). A Feature Selection Algorithm for Intrusion Detection System Based on Pigeon Inspired Optimizer. Expert Systems with Applications, 113249.
@article{alazzam2020feature, title={A Feature Selection Algorithm for Intrusion Detection System Based on Pigeon Inspired Optimizer}, author={Alazzam, Hadeel and Sharieh, Ahmad and Sabri, Khair Eddin}, journal={Expert Systems with Applications}, pages={113249}, year={2020}, publisher={Elsevier} }