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Original Article
Cross-Browser Real-Time Phishing Website Detection Framework Using Behavioral Analysis and Machine Learning
Abinaya S1
Gokulnath K2
Mohamed Irfan3
Manikandan M4
A. Raja5
1 2 3 4 B.E. Computer Science and Engineering (Cyber Security), United Institute of Technology, Coimbatore, Tamil Nadu, India. 5 Head of the Department, Department of Computer Science and Engineering (Cyber Security), United Institute of Technology, Coimbatore, Tamil Nadu, India.
Published Online: May-August 2026
Pages: 104-111
Cite this article
↗ https://www.doi.org/10.59256/indjcst.20260502010References
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15th ACM SIGKDD, Paris, France, pp. 1245–1254, 2009.
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AISec, Chicago, IL, pp. 54–60, 2010.
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pp. 1–13, 2019. doi:10.1016/j.tele.2018.09.006.
6. S. Afroz and R. Greenstadt, “PhishZoo: Detecting phishing websites by looking at them,” in Proc. 5th IEEE ICSC, Palo Alto, CA, pp. 368–
375, 2011.
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Banff, Canada, pp. 639–648, 2007.
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pp. 2091–2121, 2013. doi:10.1109/SURV.2013.032213.00009.
9. UCI Machine Learning Repository, “Phishing Websites Dataset,” M. Mohammad, F. Thabtah, and L. McCluskey (donors), 2015. [Online].
Available: https://archive.ics.uci.edu/ml/datasets/Phishing+Websites
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Management, vol. 11, no. 4, pp. 458–471, 2014.
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Systems with Applications, vol. 37, no. 12, pp. 7913–7921, 2010.
12. 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, 2019.
13. R. Mohammad, F. Thabtah, and L. McCluskey, “Predicting phishing websites using neural network trained on a thousand features,” in Proc.
IEEE/WIC/ACM WI, Atlanta, GA, 2013.
14. S. Sheng, B. Wardman, G. Warner, L. Cranor, J. Hong, and C. Zhang, “An empirical analysis of phishing blacklists,” in Proc. 6th CEAS,
Mountain View, CA, 2009.
15. Google Safe Browsing Team, “Google Safe Browsing API v4,” Google LLC, 2024. [Online]. Available: https://safebrowsing.google.com
16. PhishTank, “Phishing Website Database and API,” OpenDNS/Cisco, 2024. [Online]. Available: https://phishtank.org
17. OpenPhish, “Automated Phishing Intelligence Feed,” 2024. [Online]. Available: https://openphish.com
18. A. Y. Fu, W. Liu, X. Deng, B. Little, and G. Little, “Detecting phishing web pages with visual similarity assessment,” in Proc. RAID, 2006.
19. Scikit-learn Developers, “Scikit-learn: Machine learning in Python,” JMLR, vol. 12, pp. 2825–2830, 2011. [Online]. Available: https://scikit-
learn.org
20. FastAPI Developers, “FastAPI: High performance web framework for building APIs with Python,” 2024. [Online]. Available:
https://fastapi.tiangolo.com
21. Mozilla Developer Network, “WebExtensions API — Browser Extensions,” Mozilla Foundation, 2024. [Online]. Available:
https://developer.mozilla.org/en-US/docs/Mozilla/Add-ons/WebExtensions
22. A.Y. Fu, W. Liu, and X. Deng, “Semantic phishing: Constructing attacks on phishing-resistant protocols,” in Proc. IEEE Workshop on Web
2.0 Security and Privacy, 2006.
Symposium, San Diego, CA, 2004.
2. S. Garera, N. Provos, M. Chew, and A. D. Rubin, “A framework for detection and measurement of phishing attacks,” in Proc. ACM WORM
Workshop, Fairfax, VA, pp. 1–8, 2007.
3. J. Ma, L. K. Saul, S. Savage, and G. M. Voelker, “Beyond blacklists: Learning to detect malicious web sites from suspicious URLs,” in Proc.
15th ACM SIGKDD, Paris, France, pp. 1245–1254, 2009.
4. A. Blum, B. Wardman, T. Solorio, and G. Warner, “Lexical feature based phishing URL detection using online learning,” in Proc. ACM
AISec, Chicago, IL, pp. 54–60, 2010.
5. A. Jain and B. Gupta, “Towards detection of phishing websites on client-side using machine learning,” Telematics and Informatics, vol. 38,
pp. 1–13, 2019. doi:10.1016/j.tele.2018.09.006.
6. S. Afroz and R. Greenstadt, “PhishZoo: Detecting phishing websites by looking at them,” in Proc. 5th IEEE ICSC, Palo Alto, CA, pp. 368–
375, 2011.
7. Y. Zhang, J. Hong, and L. Cranor, “CANTINA: A content-based approach to detecting phishing web sites,” in Proc. 16th WWW Conference,
Banff, Canada, pp. 639–648, 2007.
8. M. Khonji, Y. Iraqi, and A. Jones, “Phishing detection: A literature survey,” IEEE Communications Surveys and Tutorials, vol. 15, no. 4,
pp. 2091–2121, 2013. doi:10.1109/SURV.2013.032213.00009.
9. UCI Machine Learning Repository, “Phishing Websites Dataset,” M. Mohammad, F. Thabtah, and L. McCluskey (donors), 2015. [Online].
Available: https://archive.ics.uci.edu/ml/datasets/Phishing+Websites
10. S. Marchal, J. Francois, R. State, and T. Engel, “PhishStorm: Detecting phishing with streaming analytics,” IEEE Trans. Network and Service
Management, vol. 11, no. 4, pp. 458–471, 2014.
11. M. Aburrous, M. A. Hossain, F. Thabtah, and K. Dahal, “Intelligent phishing detection system for e-banking using fuzzy data mining,” Expert
Systems with Applications, vol. 37, no. 12, pp. 7913–7921, 2010.
12. 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, 2019.
13. R. Mohammad, F. Thabtah, and L. McCluskey, “Predicting phishing websites using neural network trained on a thousand features,” in Proc.
IEEE/WIC/ACM WI, Atlanta, GA, 2013.
14. S. Sheng, B. Wardman, G. Warner, L. Cranor, J. Hong, and C. Zhang, “An empirical analysis of phishing blacklists,” in Proc. 6th CEAS,
Mountain View, CA, 2009.
15. Google Safe Browsing Team, “Google Safe Browsing API v4,” Google LLC, 2024. [Online]. Available: https://safebrowsing.google.com
16. PhishTank, “Phishing Website Database and API,” OpenDNS/Cisco, 2024. [Online]. Available: https://phishtank.org
17. OpenPhish, “Automated Phishing Intelligence Feed,” 2024. [Online]. Available: https://openphish.com
18. A. Y. Fu, W. Liu, X. Deng, B. Little, and G. Little, “Detecting phishing web pages with visual similarity assessment,” in Proc. RAID, 2006.
19. Scikit-learn Developers, “Scikit-learn: Machine learning in Python,” JMLR, vol. 12, pp. 2825–2830, 2011. [Online]. Available: https://scikit-
learn.org
20. FastAPI Developers, “FastAPI: High performance web framework for building APIs with Python,” 2024. [Online]. Available:
https://fastapi.tiangolo.com
21. Mozilla Developer Network, “WebExtensions API — Browser Extensions,” Mozilla Foundation, 2024. [Online]. Available:
https://developer.mozilla.org/en-US/docs/Mozilla/Add-ons/WebExtensions
22. A.Y. Fu, W. Liu, and X. Deng, “Semantic phishing: Constructing attacks on phishing-resistant protocols,” in Proc. IEEE Workshop on Web
2.0 Security and Privacy, 2006.
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