Reptile Search Algorithm Based Feature Selection Approach for Intrusion Detection

Maher O. Al-Khateeb, Ali Douik

2025

Abstract

In Cybersecurity, the Rise of Machine Learning (ML) Based Security Solutions Has Led to a New Era of Defense Against Evolving Threats, with Intrusion Detection (ID) Systems at the Forefront. However, the Effectiveness of These Systems Is Profoundly Influenced by the Quality and Relevance of the Input Features. the Presence of Redundant Features Can Compromise Their Performance, Making Feature Selection (FS) a Crucial Step in Optimizing ID Solutions. This Paper Uses the Reptile Search Algorithm (RSA) as a Powerful FS Method. It Offers a Gradient-Free Approach, Avoiding Local Optima and Enabling Global Optimization. Comparative Analysis Using Five Freely Available ID Datasets and Benchmarked Against Several Methods Validated Superior Performance of the RSA for ID.

Paper Citation

in Harvard Style

Al-Khateeb M. and Douik A. (2025). Reptile Search Algorithm Based Feature Selection Approach for Intrusion Detection. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-737-5, SciTePress, pages 639-645. DOI: 10.5220/0013166800003890

in Bibtex Style

@conference{icaart25,
author={Maher Al-Khateeb and Ali Douik},
title={Reptile Search Algorithm Based Feature Selection Approach for Intrusion Detection},
booktitle={Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2025},
pages={639-645},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013166800003890},
isbn={978-989-758-737-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Reptile Search Algorithm Based Feature Selection Approach for Intrusion Detection
SN - 978-989-758-737-5
AU - Al-Khateeb M.
AU - Douik A.
PY - 2025
SP - 639
EP - 645
DO - 10.5220/0013166800003890
PB - SciTePress