Enhancing Feature Selection in Network Intrusion Detection Systems Using a Novel Hybrid Binary Swarm Algorithms

Authors

Maher Khalaf Hussein, Computer Center, Presidency of the University, University of Telafer, Mosul, IraqFollow
Asmaa Alqassab, Department of Computer Science, College of Education for Pure Science, University of Mosul, Mosul, IraqFollow
Lubna Thanoon ALkahla, Software Department, Information Technology College, Ninevah University, Mosul, IraqFollow

Abstract

An intrusion detection system (IDS) is a system that monitors network traffic for any mistrustful activity and issues alerts when it is detected. The selection of feature stage is important in effective intrusion detection systems, as it determines the features that are most influential in detecting normal traffic and malicious traffic. In this study, new proposed hybrid method for selecting features in intrusion detection systems based on the hybrid binary Salp Swarm algorithm (SSA) and the binary Pigeon Inspired Optimizer (PIO), inspired by the collective behavior of animals. The proposed method removes unnecessary features and increases the number of features necessary, as the classification accuracy reached 99 percent. This feature selection approach increases the overall performance of intrusion detection systems by combining the strengths of binary SSA and PIO.

Keywords

Network Intrusion Detection Systems, NIDS, Salp Swarm, Pigeon Inspired, SSA

Subject Area

Computer Science

Article Type

Article

First Page

2429

Last Page

2437

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite this Article

Hussein, Maher Khalaf; Alqassab, Asmaa; and ALkahla, Lubna Thanoon (2025) "Enhancing Feature Selection in Network Intrusion Detection Systems Using a Novel Hybrid Binary Swarm Algorithms," Baghdad Science Journal: Vol. 22: Iss. 7, Article 27.
DOI: https://doi.org/10.21123/2411-7986.5007