Automated Phishing Website Detection and Analysis Using Advanced Machine Learning Techniques

Shanmugapriya K., Poornima D., Vasanth R., Vinoda V. R.

2025

Abstract

Phishing poses a significant danger to online security, and traditional machine learning methods such as Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) are constrained by their reliance on labeled datasets and inability to handle novel or uncommon URLs. Furthermore, these techniques are opaque, making it challenging to determine the reason behind a restricted URL. This paper proposes a rule-based system (RBS) that uses fundamental criteria, such accessibility and dubious keywords, to verify URLs. Because RBS doesn't rely on pre-existing data, it is more adaptable, efficient, and transparent than machine learning techniques for detecting phishing attempts.

Paper Citation

in Harvard Style

K. S., D. P., R. V. and R. V. (2025). Automated Phishing Website Detection and Analysis Using Advanced Machine Learning Techniques. In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25; ISBN 978-989-758-777-1, SciTePress, pages 414-420. DOI: 10.5220/0013899300004919

in Bibtex Style

@conference{icrdicct`2525,
author={Shanmugapriya K. and Poornima D. and Vasanth R. and Vinoda R.},
title={Automated Phishing Website Detection and Analysis Using Advanced Machine Learning Techniques},
booktitle={Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25},
year={2025},
pages={414-420},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013899300004919},
isbn={978-989-758-777-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25
TI - Automated Phishing Website Detection and Analysis Using Advanced Machine Learning Techniques
SN - 978-989-758-777-1
AU - K. S.
AU - D. P.
AU - R. V.
AU - R. V.
PY - 2025
SP - 414
EP - 420
DO - 10.5220/0013899300004919
PB - SciTePress