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Pane, Eddisyah Putra
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Computer Science & IT Electrical & Electronics Engineering Engineering Materials Science & Nanotechnology

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Enhanced social media phishing detection model using LSTM and BERT Syafitri, Wenni; Pane, Eddisyah Putra; Purwanto, Edi
Science, Technology, and Communication Journal Vol. 6 No. 2 (2026): SINTECHCOM Journal (February 2026)
Publisher : Lembaga Studi Pendidikan dan Rekayasa Alam Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59190/stc.v6i2.360

Abstract

Phishing attacks are a major cyber threat, with more than 30% of incidents occurring via social media platforms, especially short message services. This study evaluates deep learning approaches for automated phishing detection using BERT and Hybrid (BERT-LSTM) architectures fine-tuned on 15950 annotated SMS. The BERT-only model achieved superior performance (F1 0.9928, recall 0.9952, AUC 0.999) with no statistically significant improvement from adding BiLSTM layers (0.0006). K-fold cross-validation demonstrated robust generalisation (coefficient of variation 0.10%). Dataset saturation analysis indicated that 15,950 SMS are sufficient for effective transfer learning. Mild overfitting (6.3x loss ratio) remained within acceptable bounds and did not affect validation metrics. The 1.77% false positive rate and 99.52% recall enable practical deployment for production phishing defence. Results demonstrate that transfer learning with BERT achieves production-grade performance while challenging conventional assumptions about architectural complexity.
Co-Authors Edi Purwanto Wenni Syafitri, Wenni