A Phishing Detection System for Enhanced Cybersecurity Using Machine Learning

Adwaith Atholi Thiruvoth, Pushkar Ogale

2025

Abstract

Email phishing is a pressing cybersecurity challenge that requires efficient detection methods. Emails that look legitimate lead users to malicious sites. Our work aims to develop a machine learning-driven email classification system, named SecureInbox. A comparative study of classical machine learning techniques like Random Forest, Naive Bayes, Decision Tree, SVM, and gradient boosting regression trees was conducted, and it was found to be successful in achieving high accuracy and effectiveness in distinguishing between legitimate and phishing emails. This study makes use of various statistical methods, classification algorithms to develop a user-friendly graphical interface (GUI) for seamless email classification. SecureInbox automatically fetches the mailbox file associated with the current user in a Linux environment and classifies their emails as phishing or not phishing while displaying the results interactively. Our work helps to strengthen email security by providing a convenient tool for phishing email identification, thereby enhancing defence against cyber threats.

Paper Citation

in Harvard Style

Thiruvoth A. and Ogale P. (2025). A Phishing Detection System for Enhanced Cybersecurity Using Machine Learning. In Proceedings of the 20th International Conference on Software Technologies - Volume 1: ICSOFT; ISBN 978-989-758-757-3, SciTePress, pages 355-360. DOI: 10.5220/0013570800003964

in Bibtex Style

@conference{icsoft25,
author={Adwaith Thiruvoth and Pushkar Ogale},
title={A Phishing Detection System for Enhanced Cybersecurity Using Machine Learning},
booktitle={Proceedings of the 20th International Conference on Software Technologies - Volume 1: ICSOFT},
year={2025},
pages={355-360},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013570800003964},
isbn={978-989-758-757-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 20th International Conference on Software Technologies - Volume 1: ICSOFT
TI - A Phishing Detection System for Enhanced Cybersecurity Using Machine Learning
SN - 978-989-758-757-3
AU - Thiruvoth A.
AU - Ogale P.
PY - 2025
SP - 355
EP - 360
DO - 10.5220/0013570800003964
PB - SciTePress