URL-based phishing detection using hybrid ensemble technique - Mohd Salleh, Nurhashikin; Selamat, Siti Rahayu; Pannirchelvam, Harvinraaj; Abdollah, Mohd Faizal; Amir, Aimi Liyana (Penerbit Universiti Teknikal Malaysia Melaka, 2025)
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.