PhishGuard: A Multi-Layered Ensemble Model for Optimal Phishing Website Detection

TL;DR

PhishGuard is a multi-layered ensemble machine learning model that significantly improves phishing website detection accuracy by combining multiple classifiers and advanced feature selection techniques.

Contribution

The paper introduces PhishGuard, a novel ensemble model that integrates several classifiers with optimized feature selection and tuning for superior phishing detection.

Findings

Achieved 99.05% detection accuracy on one dataset.

Outperformed existing state-of-the-art models.

Demonstrated effectiveness of ensemble learning with optimization techniques.

Abstract

Phishing attacks are a growing cybersecurity threat, leveraging deceptive techniques to steal sensitive information through malicious websites. To combat these attacks, this paper introduces PhishGuard, an optimal custom ensemble model designed to improve phishing site detection. The model combines multiple machine learning classifiers, including Random Forest, Gradient Boosting, CatBoost, and XGBoost, to enhance detection accuracy. Through advanced feature selection methods such as SelectKBest and RFECV, and optimizations like hyperparameter tuning and data balancing, the model was trained and evaluated on four publicly available datasets. PhishGuard outperformed state-of-the-art models, achieving a detection accuracy of 99.05% on one of the datasets, with similarly high results across other datasets. This research demonstrates that optimization methods in conjunction with ensemble learning greatly improve phishing detection performance.