Enhanced Phishing Website Detection Using Optimized Ensemble Stacking Models

Phishing attacks remain a persistent and evolving cybersecurity threat, necessitating the development of highly accurate and efficient detection mechanisms. This research introduces an optimized ensemble stacking framework for phishing website detection, leveraging advanced machine learning techniques, hybrid feature preprocessing, and meta-learning strategies. The proposed approach systematically evaluates nine diverse base classifiers: XGBoost, CatBoost, LightGBM, Random Forest, Gradient Boosting, Extra Trees, Support Vector Classifier, AdaBoost, and Bagging. We compare baseline classifiers, a standard ensemble stacking model, and four optimized stacking configurations across four balanced and imbalanced datasets. Our optimized ensemble stacking achieves perfect accuracy (one hundred percent) on the first two datasets, and over ninety-nine percent accuracy on the two more challenging imbalanced datasets. A direct comparison with related studies demonstrates that our optimized stacking approach delivers superior detection accuracy.

@article{Alamri2025,
  title     = {Enhanced Phishing Website Detection Using Optimized Ensemble Stacking Models},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {8},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Zainab Alamri and Abeer Alhuzali and Bassma Alsulami and Daniyal Alghazzawi},
  doi       = {10.14569/IJACSA.2025.01608100},
  url       = {https://doi.org/10.14569/IJACSA.2025.01608100}
}