Single and Hybrid-Ensemble Learning-Based Phishing Website Detection: Examining Impacts of Varied Nature Datasets and Informative Feature Selection Technique

Abstract

To tackle issues associated with phishing website attacks, the study conducted rigorous experiments on RF, GB, and CATB classifiers. Since each classifier was an ensemble learner on their own; we integrated them into stacking and majority vote ensemble architectures to create hybrid-ensemble learning. Due to ensemble learning methods being known for their high computational time costs, the study applied the UFS technique to address these concerns and obtained promising results. Since the scalability and performance consistency of the phishing website detection system across numerous datasets is critical to combating various variants of phishing website attacks, we used three distinct phishing website datasets (DS-1, DS-2, and DS-3) to train and test each ensemble learning method to identify the best-performed one in terms of accuracy and model computational time. Our experimental findings reveal that the CATB classifier demonstrated scalable, consistent, and superior accuracy across three distinct datasets (attained 97.9% accuracy in DS-1, 97.36% accuracy in DS-2, and 98.59% accuracy in DS-3). When it comes to model computational time, the RF classifier was discovered to be the fastest when applied to all datasets, while the CATB classifier was discovered to be the second quickest when applied to all datasets.