Analysis of Gradient Boosting Algorithms with Optuna Optimization and SHAP Interpretation for Phishing Website Detection - Abu Bakar, Rahmat Fauzi; Rahardi, Majid (Politeknik Negeri Batam, 2026)
Phishing remains a persistent cybersecurity threat, evolving rapidly to bypass traditional blacklist-based detection systems. Machine Learning (ML) approaches offer a promising solution, yet finding the optimal balance between detection accuracy and model interpretability remains a challenge. This study aims to evaluate and optimize the performance of three state-of-the-art Gradient Boosting algorithms—XGBoost, LightGBM, and CatBoost—for phishing website detection. The research utilizes the UCI Phishing Websites dataset consisting of 11,055 instances. The novelty of this study lies in the implementation of the Optuna framework with the Tree-structured Parzen Estimator (TPE) for automated hyperparameter optimization and the application of SHAP (Shapley Additive Explanations) interaction values to interpret the