URL-based phishing detection using hybrid ensemble technique
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...
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| Format: | Article |
| Language: | en |
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Penerbit Universiti Teknikal Malaysia Melaka
2025
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| Online Access: | http://eprints.utem.edu.my/id/eprint/29512/2/02773020120261136232879.pdf http://eprints.utem.edu.my/id/eprint/29512/ https://jacta.utem.edu.my/jacta/article/view/5322 https://doi.org/10.54554/jacta.2025.07.02.003 |
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| author | Mohd Salleh, Nurhashikin Selamat, Siti Rahayu Pannirchelvam, Harvinraaj Abdollah, Mohd Faizal Amir, Aimi Liyana |
| author_facet | Mohd Salleh, Nurhashikin Selamat, Siti Rahayu Pannirchelvam, Harvinraaj Abdollah, Mohd Faizal Amir, Aimi Liyana |
| author_sort | Mohd Salleh, Nurhashikin |
| building | UTEM Library |
| collection | Institutional Repository |
| content_provider | Universiti Teknikal Malaysia Melaka |
| content_source | UTEM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | 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. |
| format | Article |
| id | my.utem.eprints-29512 |
| institution | Universiti Teknikal Malaysia Melaka |
| language | en |
| publishDate | 2025 |
| publisher | Penerbit Universiti Teknikal Malaysia Melaka |
| record_format | eprints |
| spelling | my.utem.eprints-295122026-02-23T01:30:38Z http://eprints.utem.edu.my/id/eprint/29512/ URL-based phishing detection using hybrid ensemble technique Mohd Salleh, Nurhashikin Selamat, Siti Rahayu Pannirchelvam, Harvinraaj Abdollah, Mohd Faizal Amir, Aimi Liyana 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. Penerbit Universiti Teknikal Malaysia Melaka 2025 Article PeerReviewed text en http://eprints.utem.edu.my/id/eprint/29512/2/02773020120261136232879.pdf 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 https://jacta.utem.edu.my/jacta/article/view/5322 https://doi.org/10.54554/jacta.2025.07.02.003 |
| spellingShingle | Mohd Salleh, Nurhashikin Selamat, Siti Rahayu Pannirchelvam, Harvinraaj Abdollah, Mohd Faizal Amir, Aimi Liyana URL-based phishing detection using hybrid ensemble technique |
| title | URL-based phishing detection using hybrid ensemble technique |
| title_full | URL-based phishing detection using hybrid ensemble technique |
| title_fullStr | URL-based phishing detection using hybrid ensemble technique |
| title_full_unstemmed | URL-based phishing detection using hybrid ensemble technique |
| title_short | URL-based phishing detection using hybrid ensemble technique |
| title_sort | url-based phishing detection using hybrid ensemble technique |
| url | http://eprints.utem.edu.my/id/eprint/29512/2/02773020120261136232879.pdf http://eprints.utem.edu.my/id/eprint/29512/ https://jacta.utem.edu.my/jacta/article/view/5322 https://doi.org/10.54554/jacta.2025.07.02.003 |
| url_provider | http://eprints.utem.edu.my/ |