Hybrid Machine Learning Framework for Phishing URL Detection with Advanced Feature Engineering

DOI

10.2991/978-94-6463-884-4_75How to use a DOI?

Keywords

Ensemble Models; Feature Engineering; Neural Networks; Phishing Detection; Porter Stemmer

Abstract

The fraudulent practice of phishing attacks targets essential user in-formation by creating fake websites that imitate authentic ones. Detection meth-ods based on traditional techniques experience difficulties when dealing with features and adaptability in addition to changing attack patterns which leads to decreased accuracy. The research assesses different machine learning models among competing text analysis techniques that include Count Vectorizer, TF-IDF Vectorizer, Porter Stemmer, and Regex Tokenization for phishing detection evaluation. Random Forest yielded 98.57% accuracy when detecting phishing websites which placed it at the top among the examined models. Strong performance of this model stems from its ability to efficiently manage dataset imbalance and deal with redundant features which leads to high classification reliability and precision. The forthcoming research agenda will explore both deep learning techniques and advanced feature selection methods alongside real-time phishing scanning capabilities to reinforce overall cybersecurity protection.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Sourav Datto
AU  - Md. Lutful Kabir
AU  - Mustakim Ahmed
AU  - Kazi Redwan
AU  - Khondaker Iffti Hasan Turjo
AU  - Md. Faruk Abdullah Al Sohan
PY  - 2025
DA  - 2025/11/18
TI  - Hybrid Machine Learning Framework for Phishing URL Detection with Advanced Feature Engineering
BT  - Proceedings of the 8th International Conference on Engineering Research, Innovation, and Education 2025 (ICERIE 2025)
PB  - Atlantis Press
SP  - 622
EP  - 628
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-884-4_75
DO  - 10.2991/978-94-6463-884-4_75
ID  - Datto2025
ER  -