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All Journal Bulletin of Electrical Engineering and Informatics
Sh. Daoud, Mohammad
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Electrical & Electronics Engineering

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A firewall model for attack detection using machine learning and metaheuristic feature selection algorithms Abualhaj, Mosleh M.; Al-Khatib, Sumaya Nabil; Al-Shafi, Nida; Hiari, Mohammad O.; Sh. Daoud, Mohammad; Anbar, Mohammed; Al-Zyoud, Mahran M.
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.9887

Abstract

This research presents a firewall model designed to enhance network attack detection by integrating machine learning (ML) and advanced feature selection techniques. The study introduces a union-based (DAUBA) feature selection method that combines the exploratory capability of the Dragonfly Algorithm (DA) with the exploitation efficiency of the Bat Algorithm (BA). By combining these two bio-inspired optimizers, the method generates complementary feature subsets that enhance both accuracy and efficiency. The proposed DA?BA feature selection method is incorporated into a ML–based firewall and evaluated on the UNSW-NB15 dataset using three classifiers: adaptive boosting (AdaBoost), K-nearest neighbor (KNN), and Naïve Bayes (NB). Experimental results demonstrate that the approach achieves near-perfect accuracy (100% with AdaBoost), along with strong precision, recall, and F1-scores, while maintaining computational costs compatible with real-time deployment. These findings highlight the novelty and practical value of combining DA and BA in feature selection for next-generation firewall systems.
Co-Authors Abualhaj, Mosleh M. Al-Khatib, Sumaya Nabil Al-Shafi, Nida Al-Zyoud, Mahran M. Anbar, Mohammed Hiari, Mohammad O.