URL-based phishing detection using hybrid ensemble technique

Mohd Salleh, Nurhashikin and Selamat, Siti Rahayu and Pannirchelvam, Harvinraaj and Abdollah, Mohd Faizal and Amir, Aimi Liyana (2025) URL-based phishing detection using hybrid ensemble technique. Journal of Advanced Computing Technology and Application (JACTA), 7 (2). pp. 30-45. ISSN 2672-7188

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Abstract

Phishing attacks pose a significant and growing threat to cybersecurity by deceiving users into disclosing sensitive information through malicious websites. Most existing URL-based phishing detection studies rely on individual classifiers or traditional ensemble techniques, which often struggle to generalize against evolving phishing patterns. To overcome this limitation, this study proposes a hybrid ensemble learning approach for UR based phishing detection by integrating a Random Forest Classifier with AdaBoost, Bagging, and Stacking strategies. Experiments were conducted using a publicly available benchmark dataset from the UCI Machine Learning Repository consisting of 11,055 URLs and 30 features. Model performance was evaluated using 10-fold cross-validation. The results show that the Random Forest–Stacking hybrid model achieved the highest accuracy of 97.21%, outperforming other hybrid configurations. The findings demonstrate that stacking-based hybrid ensemble learning enhances generalization and robustness in phishing URL detection.

Item Type:	Article
Uncontrolled Keywords:	Phishing attacks, Hybrid ensemble technique, Uniform resource locator (URL), WEKA (Waikato environment for knowledge analysis)
Divisions:	Faculty of Artificial Intelligence and Cyber Security
Depositing User:	Sabariah Ismail
Date Deposited:	23 Feb 2026 01:30
Last Modified:	23 Feb 2026 01:30
URI:	http://eprints.utem.edu.my/id/eprint/29512
Statistic Details:	View Download Statistic

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