Design of a Phishing Detection System Using URL-Based Hybrid Machine Learning

Authors

  • Dr Y. Rokesh Kumar,Dr SK. Mulla Shabbeer|Mrs D. Surekha|I.Spandan Pradeep.

Keywords:

Intelligent Detection, Phishing Websites, Hybrid Machine Learning, URL Features, Phishing Detection, Feature Selection Logistic Regression, Support Vector Machine, Decision Tree

Abstract

Phishing websites continue to pose a major cyber security threat by using deceptiveURLs to lure users into disclosing sensitive information. To counter these increasinglysophisticated attacks, this study presents an intelligent hybrid machine learning model—referred

References

Kaur, M., & Singh, A. (2021). Detection of phishing URLs using ensemble classification techniques. Journal of Cybersecurity and Digital Forensics, 9(2), 101–110.

Thomas, K., & Sinha, R. (2020). Hybrid approaches for identifying malicious links using structured URL analysis. Proceedings of the International Conference on Cyber Intelligence, 57–66.

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Published

2025-06-27

How to Cite

Dr Y. Rokesh Kumar,Dr SK. Mulla Shabbeer|Mrs D. Surekha|I.Spandan Pradeep. (2025). Design of a Phishing Detection System Using URL-Based Hybrid Machine Learning . Journal of Computational Analysis and Applications (JoCAAA), 34(6), 151–161. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/3067

Issue

Vol. 34 No. 6 (2025): JOCAAA

Section

Articles

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.