Klasifikasi URL Phishing untuk SIEM: Perbandingan Model Machine Learning XGBoost dan Deep Learning TabNet dalam Deteksi Ancaman Siber - Tjahjono, Azza Farichi; Hasan, Hasan; Putera, Randist Prawandha; Indranto, Dionisius Marcell Putra; Hermawan, Abhirama Triadyatma (Sekolah Tinggi Agama Islam Nurul Islam Mojokerto, 2025)
Phishing detection is a criticalcomponent of modern Security Information and Event Management (SIEM) systems, requiring both high accuracy and real-time performance. This study conducts a comprehensive comparison between a Gradient-Boosted Decision Tree model, XGBoost, and a deep learning architecture, TabNet, for classifying phishing URLs. Both models were systematicallyoptimized using advanced hyperparameter tuning techniques, Randomized Search for XGBoost and Optuna with pruning for TabNetto ensure a fair and robust evaluation. The models were trained and tested on the