A Real-Time Phishing Detection System: A Web-Based Solution for Enhanced Cybersecurity

Chitturu Sudheer, Sridevi Sakhamuri, Gaadhe Naveen, Sala Vidwath Sai

2025

Abstract

Phishing is a deceptive online scam where attackers trick users by sending fake messages that seem to be from reliable sources. These messages usually contain URLs or attachments intended to steal confidential details or compromise systems with malware upon interaction. While conventional phishing techniques relied on mass spam campaigns targeting a broad audience, modern approaches have become more sophisticated. To combat phishing effectively, machine learning provides a powerful approach by categorizing URLs as either malicious or safe. By examining different URL attributes, algorithms such as SVM, DL architectures like Neural Networks, along with Random Forest and Decision Trees, and XGBoost have been employed in detection systems. This study proposes a gradient boosting classifier-based method for real-time phishing URL detection. The approach leverages distinct URL characteristics to distinguish genuine links from fraudulent ones, demonstrating significant effectiveness in accurately identifying phishing attempts in real-time scenarios.

Paper Citation

in Harvard Style

Sudheer C., Sakhamuri S., Naveen G. and Sai S. (2025). A Real-Time Phishing Detection System: A Web-Based Solution for Enhanced Cybersecurity. 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 19-25. DOI: 10.5220/0013891000004919

in Bibtex Style

@conference{icrdicct`2525,
author={Chitturu Sudheer and Sridevi Sakhamuri and Gaadhe Naveen and Sala Sai},
title={A Real-Time Phishing Detection System: A Web-Based Solution for Enhanced Cybersecurity},
booktitle={Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25},
year={2025},
pages={19-25},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013891000004919},
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 - A Real-Time Phishing Detection System: A Web-Based Solution for Enhanced Cybersecurity
SN - 978-989-758-777-1
AU - Sudheer C.
AU - Sakhamuri S.
AU - Naveen G.
AU - Sai S.
PY - 2025
SP - 19
EP - 25
DO - 10.5220/0013891000004919
PB - SciTePress