Title
PhishGuard: A Multi‑Layered Ensemble Model for Optimal Phishing Website Detection
Authors
Md Sultanul Islam Ovi · Md Hasibur Rahman · Mohammad Arif Hossain
Abstract
Phishing attacks are a growing cybersecurity threat that rely on deceptive websites to steal sensitive information. This paper introduces PhishGuard, a custom multi-layered ensemble model that improves phishing site detection. The model integrates multiple machine learning classifiers including Random Forest, Gradient Boosting, CatBoost, and XGBoost. It uses advanced feature selection methods such as SelectKBest and RFECV, along with hyperparameter tuning and data balancing techniques. PhishGuard was trained and evaluated using four publicly available datasets and outperformed state-of-the-art models, achieving up to 99.05% accuracy. The results show that combining optimization methods with ensemble learning significantly enhances phishing detection performance.
Conference
2024 6th International Conference on Sustainable Technologies for Industry 5.0 (STI), IEEE
PDF
IEEE Xplore Link
BibTeX
@inproceedings{ovi2024phishguard,
title={PhishGuard: A Multi-Layered Ensemble Model for Optimal Phishing Website Detection},
author={Ovi, Md Sultanul Islam and Rahman, Md Hasibur and Hossain, Mohammad Arif},
booktitle={2024 6th International Conference on Sustainable Technologies for Industry 5.0 (STI)},
pages={1--6},
year={2024},
organization={IEEE}
}
