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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Reconfigurable and Embedded Systems (IJRES) Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science Journal of Applied Data Sciences
Abualhaj, Mosleh M.
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering

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Improving firewall performance using hybrid of optimization algorithms and decision trees classifier Abualhaj, Mosleh M.; Abu-Shareha, Ahmad Adel; Al-Khatib, Sumaya Nabil; Alsaaidah, Adeeb M.; Anbar, Mohammed
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp2839-2848

Abstract

One of the primary concerns of governments, corporations, and even individual users is their level of online protection. This is because a large number of attacks target their primary assets. A firewall is a critical tool that almost every organization uses to protect its assets. However, firewalls become less reliable when they deal with large amounts of data. One method for reducing the amount of data and enhancing firewall performance is feature selection. The main aim of this study is to enhance the firewall's performance by proposing a new feature selection method. The proposed feature selection method combines the strengths of Harris Hawks optimization (HHO) and whale optimization algorithm (WOA). Experiments were performed utilizing the NSL-KDD dataset to measure the effectiveness of the proposed method. The experiments employed the decision trees (DTs) as a machine classifier. The experimental results show that the achieved accuracy is 98.46% when using HHO/WOA for feature selection and DT for classification, outperforming the HHO and WOA when used separately for feature selection. The study's findings offer insightful information for researchers and practitioners looking to improve firewall effectiveness and efficiency in defending internet connections against changing threats.
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 Abu-Shareha, Ahmad Abu-Shareha, Ahmad A. Abu-Shareha, Ahmad Adel Al-Khatib, Sumaya Al-Khatib, Sumaya N Al-Khatib, Sumaya N. Al-Khatib, Sumaya Nabil Al-Shafi, Nida Al-Shamayleh, Ahmad Sami Al-Zyoud, Mahran M. Alalousi, Alabass Alkhatib, Sumaya Nabil Alsaaidah, Adeeb Alsaaidah, Adeeb M. Alsharaiah, Mohammad A Amer, Amal Anbar, Mohammed Fadhil, Hilal A. Hiari, Mohammad O Hiari, Mohammad O. Munther, Alhamza Nabil Alkhatib, Sumaya Rateb, Roqia Sh. Daoud, Mohammad Shambour, Qusai Y.