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All Journal CESS (Journal of Computer Engineering, System and Science) EXPLORE International Journal of Informatics and Computation Jurnal Teknimedia: Teknologi Informasi dan Multimedia Jurnal Pengembangan Sistem Informasi dan Informatika Jurnal Teknologi Kharisma Tech EXPLORE Computing and Education Technology Journal Coreid Journal Jurnal Inovasi Teknologi Terapan JURNAL FLOBAMORATA MENGABDI International Journal of Informatics Engineering and Computing
M. Hizbul Wathan
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Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Mechanical Engineering Social Sciences Other

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Journal : coreid journal

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PhishTect: A Hybrid SMOTEN–FT-Transformer–PSO Framework for Enhanced Phishing Website Detection on Imbalanced Data M Hizbul Wathan; Muhammad Kalam Sabili
CoreID Journal Vol. 4 No. 2 (2026): July 2026
Publisher : CV. Generasi Intelektual Digital

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60005/coreid.v4i2.176

Abstract

Phishing websites remain a major cybersecurity threat, while conventional blacklist-based detection systems often fail to identify newly emerging and zero-day attacks. In addition, phishing datasets are frequently imbalanced, causing machine learning models to exhibit poor minority-class detection performance. To address these challenges, this study proposes PhishTect, a hybrid phishing detection framework that integrates Synthetic Minority Oversampling for Nominal Data (SMOTEN), Feature Tokenizer Transformer (FT-Transformer), and Particle Swarm Optimization (PSO). Unlike existing approaches that typically focus on balancing, deep learning, or optimization techniques separately, the proposed framework combines these components within a unified architecture. SMOTEN is employed to balance categorical phishing data, FT-Transformer learns contextual representations from tabular features, and PSO optimizes model hyperparameters to improve predictive capability. Experiments conducted on the PhishTank dataset evaluated three scenarios: baseline FT-Transformer, FT-Transformer with SMOTEN, and the proposed SMOTEN–FT-Transformer–PSO framework. The proposed model achieved the best performance, obtaining 96.62% accuracy, 0.9696 F1-score, 0.9963 ROC-AUC, and 0.997 PR-AUC. The results demonstrate that integrating oversampling, Transformer-based feature learning, and swarm intelligence optimization significantly improves phishing detection effectiveness, robustness, and generalization on imbalanced cybersecurity datasets.
Co-Authors Ahmat Josi Ali, Hendrik Aulia, Rosaura Better Swengky Bradika Almandin Wisesa Cahyadi, Irsan Dede Hamdani Deswita Deswita Efendi, Muhamad Masjun Evvin Faristasari Fadhilah, Indirokan Fadli, Sofiansyah Fadli Fatir Atthariq Zami Indra Irawan Indra Irawan Irwan Ivansius Nahak Khemal Fasyah Ishaq Lesta, Lesta LINDA FUJIYANTI Moh. Aziz Muhammad Kalam Sabili Oktaviani, Ajeng Prayogi, M. Bagus Ramadani, Ardi Rani Laple Satria Rani Laple Satria Putra Riki Afriyansyah Riski Meliya Ningsih Satria Agus Darma Silvia Agustin Sirlus Andreanto Jasman Duli Sofiansyah Fadli Tegar Priyadi Wardianto, Wardianto Yang Agita Rindri Zain, M Syafrizal