<![CDATA[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 "black-box" models. The experimental results demonstrate that the LightGBM model, optimized via Optuna, achieved the highest performance with an F1-Score of 0.9798, outperforming the baseline model (0.9713) by 0.87%. Furthermore, SHAP analysis identified 'SSLfinal_State' as the most critical determinant for distinguishing phishing sites. This study confirms that optimizing modern boosting algorithms significantly enhances phishing detection capabilities while providing necessary explainability for cybersecurity analysts.]]>
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