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- Vol. 5 Issue 3
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An Explainable Machine Learning Framework for Phishing Website Detection Using URL and Network Features
Authors
1 2 Department of Computer Science, Bhonsala Military College, Nashik, Maharashtra, India.
Article Information
- Published In
- Volume 5, Issue 3 (2026)
- Published Online
- September-December 2026
- Pages
- 371-381
- Article Type
- Original Article
References
1. Anti-Phishing Working Group (APWG), 'Phishing Activity Trends Report, 4th Quarter 2023,'
2. APWG, Tech. Rep., 2023. [2]. Verizon Business, '2024 Data Breach Investigations Report (DBIR),' Verizon, Tech. Rep., 2024.
3. D. Sahoo, C. Liu, and S. C. H. Hoi, 'Malicious URL detection using machine learning: A survey,' arXiv:1701.07179, 2017.
4. R. S. Rao and A. R. Pais, 'Detection of phishing websites using an efficient feature-based machine learning framework,' Neural
Comput. & Appl., vol. 31, no. 8, pp. 3851-3873, 2019.
5. A. Hannousse and S. Yahiouche, 'Towards benchmark datasets for machine learning based website phishing detection: An
experimental study,' Eng. Appl. Artif. Intell., vol. 104, p. 104347, 2021.
6. S. Marchal et al., 'Off-the-hook: An efficient and usable client-side phishing prevention application,' IEEE Trans. Comput., vol. 66,
no. 10, pp.1717-1733, 2017.
7. O. K. Sahingoz, E. Buber, O. Demir, and B. Diri, 'Machine learning based phishing detection from URLs,' Expert Syst. Appl., vol.
117, pp.345-357, 2019.
8. M. Babagoli, M. P. Aghababa, and V. Solouk, 'Heuristic nonlinear regression strategy for detecting phishing websites,' Soft Comput.,
vol. 23,pp. 4315-4327, 2019.
9. C. Opara, B. Wei, and Y. Chen, 'HTMLPhish: Enabling phishing web page detection by applying deep learning techniques on HTML
analysis,' in Proc. IJCNN, 2020, pp. 1-8.
10. M. A. Adebowale, K. T. Lwin, and M. A. Hossain, 'Intelligent phishing detection scheme using deep learning algorithms,' J.
Enterp. Inf.Manag., vol. 36, no. 3, pp. 747-766, 2023.
11. G. Vrbančič, I. Fister Jr, and V. Podgorelec, 'Datasets for phishing websites detection,' Data in Brief, vol. 33, p. 106438, 2020.
12. T. Chen and C. Guestrin, 'XGBoost: A scalable tree boosting system,' in Proc. ACM SIGKDD, 2016, pp. 785-794.
13. S. M. Lundberg et al., 'From local explanations to global understanding with explainable AI for trees,' Nature Mach. Intell., vol. 2,
no. 1, pp.56-67, 2020.
14. S. M. Lundberg and S.-I. Lee, 'A unified approach to interpreting model predictions,' in NeurIPS, vol. 30, pp. 4765-4774, 2017.
15. R. M. Mohammad, F. Thabtah, and L. McCluskey, 'Predicting phishing websites based on self-structuring neural network,' Neural
Comput. & Appl., vol. 25, no. 2, pp. 443-458, 2014.
16. A. Al-Alyan and S. Al-Ahmadi, 'Robust URL phishing detection based on deep learning,' KSII TIIS, vol. 14, no. 7, pp. 2752-2768,
2020.
17. K. L. Chiew et al., 'A new hybrid ensemble feature selection framework for machine learning-based phishing detection system,' Inf.
Sci., vol.484, pp. 153-166, 2019.
18. M. Al-Fayoumi, B. Alhijawi, Q. Abu Al-Haija, and R. Armoush, 'XAI-PhD: Fortifying trust of phishing URL detection empowered by
Shapley additive explanations,' iJOE, vol. 20, no. 11, pp. 80-101, 2024.
19. K. M. M. Uddin et al., 'Explainable machine learning for phishing site detection: A high-efficiency approach using boosting
models and SHAP,' J. Eng., vol. 2025, no. 1, 2025.
20. M. C. Calzarossa, P. Giudici, and R. Zieni, 'Explainable machine learning for phishing feature detection,' Qual. Reliab. Eng. Int., vol.
40, no. 1,pp. 362-373, 2024.
21. T. Kehkashan et al., 'Explainable phishing website detection for secure and sustainable cyber infrastructure,' Sci. Rep., vol. 15, p.
41751, 2025.
2. APWG, Tech. Rep., 2023. [2]. Verizon Business, '2024 Data Breach Investigations Report (DBIR),' Verizon, Tech. Rep., 2024.
3. D. Sahoo, C. Liu, and S. C. H. Hoi, 'Malicious URL detection using machine learning: A survey,' arXiv:1701.07179, 2017.
4. R. S. Rao and A. R. Pais, 'Detection of phishing websites using an efficient feature-based machine learning framework,' Neural
Comput. & Appl., vol. 31, no. 8, pp. 3851-3873, 2019.
5. A. Hannousse and S. Yahiouche, 'Towards benchmark datasets for machine learning based website phishing detection: An
experimental study,' Eng. Appl. Artif. Intell., vol. 104, p. 104347, 2021.
6. S. Marchal et al., 'Off-the-hook: An efficient and usable client-side phishing prevention application,' IEEE Trans. Comput., vol. 66,
no. 10, pp.1717-1733, 2017.
7. O. K. Sahingoz, E. Buber, O. Demir, and B. Diri, 'Machine learning based phishing detection from URLs,' Expert Syst. Appl., vol.
117, pp.345-357, 2019.
8. M. Babagoli, M. P. Aghababa, and V. Solouk, 'Heuristic nonlinear regression strategy for detecting phishing websites,' Soft Comput.,
vol. 23,pp. 4315-4327, 2019.
9. C. Opara, B. Wei, and Y. Chen, 'HTMLPhish: Enabling phishing web page detection by applying deep learning techniques on HTML
analysis,' in Proc. IJCNN, 2020, pp. 1-8.
10. M. A. Adebowale, K. T. Lwin, and M. A. Hossain, 'Intelligent phishing detection scheme using deep learning algorithms,' J.
Enterp. Inf.Manag., vol. 36, no. 3, pp. 747-766, 2023.
11. G. Vrbančič, I. Fister Jr, and V. Podgorelec, 'Datasets for phishing websites detection,' Data in Brief, vol. 33, p. 106438, 2020.
12. T. Chen and C. Guestrin, 'XGBoost: A scalable tree boosting system,' in Proc. ACM SIGKDD, 2016, pp. 785-794.
13. S. M. Lundberg et al., 'From local explanations to global understanding with explainable AI for trees,' Nature Mach. Intell., vol. 2,
no. 1, pp.56-67, 2020.
14. S. M. Lundberg and S.-I. Lee, 'A unified approach to interpreting model predictions,' in NeurIPS, vol. 30, pp. 4765-4774, 2017.
15. R. M. Mohammad, F. Thabtah, and L. McCluskey, 'Predicting phishing websites based on self-structuring neural network,' Neural
Comput. & Appl., vol. 25, no. 2, pp. 443-458, 2014.
16. A. Al-Alyan and S. Al-Ahmadi, 'Robust URL phishing detection based on deep learning,' KSII TIIS, vol. 14, no. 7, pp. 2752-2768,
2020.
17. K. L. Chiew et al., 'A new hybrid ensemble feature selection framework for machine learning-based phishing detection system,' Inf.
Sci., vol.484, pp. 153-166, 2019.
18. M. Al-Fayoumi, B. Alhijawi, Q. Abu Al-Haija, and R. Armoush, 'XAI-PhD: Fortifying trust of phishing URL detection empowered by
Shapley additive explanations,' iJOE, vol. 20, no. 11, pp. 80-101, 2024.
19. K. M. M. Uddin et al., 'Explainable machine learning for phishing site detection: A high-efficiency approach using boosting
models and SHAP,' J. Eng., vol. 2025, no. 1, 2025.
20. M. C. Calzarossa, P. Giudici, and R. Zieni, 'Explainable machine learning for phishing feature detection,' Qual. Reliab. Eng. Int., vol.
40, no. 1,pp. 362-373, 2024.
21. T. Kehkashan et al., 'Explainable phishing website detection for secure and sustainable cyber infrastructure,' Sci. Rep., vol. 15, p.
41751, 2025.
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