ERGA-Phish: Optimized Ensemble Learning Framework with Ridge Regression and Genetic Algorithm for Improved Phishing URL Detection

Abstract

Phishing remains a persistent cybersecurity threat that exploits deceptive web links to compromise users and organizations. Although machine learning and deep learning techniques have demonstrated strong phishing detection capabilities, challenges related to class imbalance, feature redundancy, generalization, and model optimization remain. This study proposes an optimized stacking ensemble framework that integrates Decision Tree, Random Forest, Naïve Bayes, XGBoost, and Artificial Neural Network (ANN) classifiers, with an ANN serving as the meta-learner. The PhiUSIIL Phishing URL Dataset, obtained from the UCI Machine Learning Repository, contains 235,795 labeled URLs, comprising 134,850 legitimate and 100,945 phishing URLs. Data preprocessing involved duplicate removal, mean imputation, one-hot encoding, normalization, and Synthetic Minority Oversampling Technique (SMOTE), producing a balanced dataset of 269,700 instances. Ridge Regression was then applied to reduce the feature space from 57 to 50 relevant features. The stacking ensemble was further optimized using a Genetic Algorithm (GA) to identify suitable parameter configurations for improved predictive performance. Using an 80:20 train-test split, the proposed model achieved 99.54% accuracy, 98.75% precision, 98.85% recall, 98.68% F1-score, and 0.98 ROC-AUC. The proposed model outperformed the evaluated baseline classifiers and most benchmarked studies, although one reported approach achieved 99.89% accuracy. The findings demonstrate that combining heterogeneous classifiers, feature selection, class balancing, and evolutionary optimization can provide a strong framework for phishing URL detection.