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All Journal Sains Data Jurnal Studi Matematika dan Teknologi
Hermawan, Abhirama Triadyatma
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Computer Science & IT Electrical & Electronics Engineering Engineering Mathematics Physics

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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
Sains Data Jurnal Studi Matematika dan Teknologi Vol 3, No 2: July-December 2025 (On Progress)
Publisher : Sekolah Tinggi Agama Islam Nurul Islam Mojokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52620/sainsdata.v3i2.227

Abstract

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 "Dataset of Suspicious Phishing URL Detection," a recent and relevant collection of URL features. The resultsdemonstrate that the tunedXGBoost model significantly outperforms the tunedTabNet model across all key metrics. Furthermore, inference speed analysis revealedXGBoostto besubstantially moreefficient on both CPU and GPU hardware, with a GPU inference time over 33 times faster thanTabNet. These findings lead to the conclusion that for this task,XGBoostoffers a superior combination of accuracy, speed, and practicaldeployability,making it the more suitable architecture for integration into a SIEM system. 
Co-Authors Hasan Hasan Indranto, Dionisius Marcell Putra Putera, Randist Prawandha Tjahjono, Azza Farichi