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Original Article
Explainable Phishing URL Detection Using Ensemble Learning and SHAP-Based Feature Attribution
Arpita Ghetiya1
Harsh Aghera2
Tejaswi Telkar3
1 2 3 Department of CSE, Dayananda Sagar University, Bangalore, Karnataka, India.
Published Online: May-August 2026
Pages: 58-63
Cite this article
↗ https://www.doi.org/10.59256/indjcst.20260502006References
[1] Anti-Phishing Working Group (APWG), “Phishing Activity Trends Report, Q4 2023,” Tech. Rep., APWG, 2024. [Online]. Available:
https://apwg.org/trendsreports/
[2] Federal Bureau of Investigation, “Internet Crime Report 2023,” Internet Crime Complaint Center (IC3), 2024. [Online]. Available:
https://www.ic3.gov/Media/PDF/AnnualReport/2023_IC3Report.pdf
[3] T. Moore and R. Clayton, “Examining the Impact of Website Take-down on Phishing,” in Proc. APWG eCrime Researchers Summit,
Pittsburgh, PA, USA, Oct. 2007, pp. 1–13.
[4] S. M. Lundberg and S.-I. Lee, “A Unified Approach to Interpreting Model Predictions,” in Advances in Neural Information Processing
Systems (NeurIPS), vol. 30, 2017, pp. 4765–4774.
[5] S. M. Lundberg, G. G. Erion, and S.-I. Lee, “Consistent Individualized Feature Attribution for Tree Ensembles,” arXiv preprint
arXiv:1802.03888, 2018.
[6] O. K. Sahingoz, E. Buber, O. Demir, and B. Diri, “Machine Learning Based Phishing Detection from URLs,” Expert Systems with
Applications, vol. 117, pp. 345–357, Mar. 2019.
[7] R. M. Mohammad, F. Thabtah, and L. McCluskey, “An Assessment of Features Related to Phishing Websites Using an Automated
Technique,” in Proc. Int. Conf. Internet Technology and Secured Transactions (ICITST), London, UK, 2012, pp. 492–497.
[8] R. S. Rao and A. R. Pais, “Detection of Phishing Websites Using an Efficient Feature-Based Machine Learning Framework,” Neural
Computing and Applications, vol. 31, no. 8, pp. 3851–3873, Aug. 2019.
[9] A. S. Bozkir, E. A. Sezer, and M. Gunes, “Phishing Web Page Detection Using Transformer-Based Language Models,” Computers &
Security, vol. 128, Art. no. 103147, 2023.
[10] W. Yang, J. Zuo, and B. Cui, “Phishing Website Detection Based on Multidimensional Features Driven by Deep Learning,” IEEE Access,
vol. 9, pp. 15196–15209, 2021.
[11] G. Vrbancic, I. Fister Jr., and V. Podgorelec, “Datasets for Phishing Websites Detection,” Data in Brief, vol. 33, Art. no. 106438, Dec. 2020.
[12] M. Mahdi, A. Al-Dujaili, and U. O’Reilly, “Interpretable Malware Detection Using Explainable Artificial Intelligence,” in Proc. IEEE Int.
Conf. Cyber Security and Resilience (CSR), 2022, pp. 210–217.
[13] A. Rozsa, T. E. Boult, and M. Gunther, “SHAP-Based Explanation for Network Intrusion Detection,” in Proc. IEEE Int. Conf.
Communications and Network Security (CNS), 2023, pp. 1–9.
[14] A. Oest, P. Safaei, A. Doupe, G.-J. Ahn, B. Wardman, and G. Warner, “PhishTime: Continuous Longitudinal Measurement of the
Effectiveness of Anti-Phishing Blacklists,” in Proc. USENIX Security Symposium, 2020, pp. 379–396.
[15] L. Breiman, “Random Forests,” Machine Learning, vol. 45, no. 1, pp. 5–32, Oct. 2001.
[16] B. Prasad and A. Bhatt, “PhiUSIIL Phishing URL Dataset,” UCI Machine Learning Repository, 2023. [Online]. Available:
https://archive.ics.uci.edu/dataset/967
[17] OpenDNS, “PhishTank Developer Information,” 2024. [Online]. Available: https://www.phishtank.com/developer_info.php
https://apwg.org/trendsreports/
[2] Federal Bureau of Investigation, “Internet Crime Report 2023,” Internet Crime Complaint Center (IC3), 2024. [Online]. Available:
https://www.ic3.gov/Media/PDF/AnnualReport/2023_IC3Report.pdf
[3] T. Moore and R. Clayton, “Examining the Impact of Website Take-down on Phishing,” in Proc. APWG eCrime Researchers Summit,
Pittsburgh, PA, USA, Oct. 2007, pp. 1–13.
[4] S. M. Lundberg and S.-I. Lee, “A Unified Approach to Interpreting Model Predictions,” in Advances in Neural Information Processing
Systems (NeurIPS), vol. 30, 2017, pp. 4765–4774.
[5] S. M. Lundberg, G. G. Erion, and S.-I. Lee, “Consistent Individualized Feature Attribution for Tree Ensembles,” arXiv preprint
arXiv:1802.03888, 2018.
[6] O. K. Sahingoz, E. Buber, O. Demir, and B. Diri, “Machine Learning Based Phishing Detection from URLs,” Expert Systems with
Applications, vol. 117, pp. 345–357, Mar. 2019.
[7] R. M. Mohammad, F. Thabtah, and L. McCluskey, “An Assessment of Features Related to Phishing Websites Using an Automated
Technique,” in Proc. Int. Conf. Internet Technology and Secured Transactions (ICITST), London, UK, 2012, pp. 492–497.
[8] R. S. Rao and A. R. Pais, “Detection of Phishing Websites Using an Efficient Feature-Based Machine Learning Framework,” Neural
Computing and Applications, vol. 31, no. 8, pp. 3851–3873, Aug. 2019.
[9] A. S. Bozkir, E. A. Sezer, and M. Gunes, “Phishing Web Page Detection Using Transformer-Based Language Models,” Computers &
Security, vol. 128, Art. no. 103147, 2023.
[10] W. Yang, J. Zuo, and B. Cui, “Phishing Website Detection Based on Multidimensional Features Driven by Deep Learning,” IEEE Access,
vol. 9, pp. 15196–15209, 2021.
[11] G. Vrbancic, I. Fister Jr., and V. Podgorelec, “Datasets for Phishing Websites Detection,” Data in Brief, vol. 33, Art. no. 106438, Dec. 2020.
[12] M. Mahdi, A. Al-Dujaili, and U. O’Reilly, “Interpretable Malware Detection Using Explainable Artificial Intelligence,” in Proc. IEEE Int.
Conf. Cyber Security and Resilience (CSR), 2022, pp. 210–217.
[13] A. Rozsa, T. E. Boult, and M. Gunther, “SHAP-Based Explanation for Network Intrusion Detection,” in Proc. IEEE Int. Conf.
Communications and Network Security (CNS), 2023, pp. 1–9.
[14] A. Oest, P. Safaei, A. Doupe, G.-J. Ahn, B. Wardman, and G. Warner, “PhishTime: Continuous Longitudinal Measurement of the
Effectiveness of Anti-Phishing Blacklists,” in Proc. USENIX Security Symposium, 2020, pp. 379–396.
[15] L. Breiman, “Random Forests,” Machine Learning, vol. 45, no. 1, pp. 5–32, Oct. 2001.
[16] B. Prasad and A. Bhatt, “PhiUSIIL Phishing URL Dataset,” UCI Machine Learning Repository, 2023. [Online]. Available:
https://archive.ics.uci.edu/dataset/967
[17] OpenDNS, “PhishTank Developer Information,” 2024. [Online]. Available: https://www.phishtank.com/developer_info.php
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