Comparative Analysis of Undersampling, Oversampling, and SMOTE Techniques for Addressing Class Imbalance in Phishing Website Detection
Kamal Omari, Chaimae Taoussi, Ayoub Oukhatar · International Journal of Advanced Computer Science and Applications · 2025
Since this is one of the most challenging tasks in cyber security, many of them are affected by class imbalance when it comes to the performance of machine learning. This paper evaluates various strategies using a number of resampling-based approaches: ROS, RUS, and SMOTE-based methods in conjunction with XGBoost classifier techniques to solve such an imbalanced dataset. Key performance measures include precision, F1 score, recall, precision, ROC-AUC, and geometric mean score. Among the methods, the highest was found with regard to the SMOTE-NC-XGB with precision equal to 98.0% and a recall of 98.5%, thus ensuring an effective trade-off between sensitivity and specificity. Although the stand-alone XGB model performs really well, adding resampling techniques makes its efficiency much higher, especially in cases of evident imbalance between classes. These results also revealed that resampling techniques are really helpful to enhance detection performance; hence, the SMOTE-NC-XGB is found out as the best among all of these. It will be of great contribution for future works in order to enhance the development of phishing detection systems and investigate other new hybrid resampling methods.