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All Journal Bulletin of Electrical Engineering and Informatics
B. Alzghoul, Musab
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Electrical & Electronics Engineering

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A powerful machine learning method for detecting phishing threats Baklizi, Mahmoud; Zraqou, Jamal; Alkhazaleh, Mohammad; Atoum, Issa; Alzyoud, Faisal; B. Alzghoul, Musab
Bulletin of Electrical Engineering and Informatics Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i6.9579

Abstract

Phishing threats exploit social engineering and deceptive web infrastructure to steal sensitive personal information, often by mimicking legitimate websites. With the proliferation of online services and the increasing prevalence of cybercrime, detecting phishing websites has become a critical challenge. This study presents a comprehensive machine learning (ML)-based approach for detecting phishing websites. A total of 48 discriminative features were extracted from 10,000 web pages—comprising 5,000 phishing and 5,000 legitimate sites. Nine ML classifiers were initially evaluated, including random forest (RF), support vector machine (SVM), and XGBoost. Ensemble models based on soft voting and stacking were then constructed to improve detection performance. Among the models, the soft voting classifier (VC) achieved the best performance with an accuracy and F1-score of 98.82%. The results indicate that ensemble learning offers a robust solution for the automated detection of phishing websites.
Co-Authors Alkhazaleh, Mohammad Alzyoud, Faisal Atoum, Issa Baklizi, Mahmoud Zraqou, Jamal