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This paper discusses the development of a mobile application that analyzes and detects Tagalog-English smishing attacks using the Bidirectional Encoder Representations from Transformers (BERT) model integrated into the specified Cross-Industry Standard Process for Data Mining (CRISP-DM) frame-work. The Tagalog-English datasets from Kaggle and the National Telecommunications Commission (NTC) were analyzed using the BERT model, with an accuracy 98. 10% for mBERT and 98.29% for distilBERT.

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SMSegurado: A Mobile-Based Application for Analyzing and Mitigating Tagalog-English SMiShing Attacks Using BERT-Based Model as SMS Content Analyzer

  • 会议论文

  • 发表日期：

    2025年

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  • Mecca Cristel T. Allego,
  • Peaches Ann A. Diez,
  • Jhon Mark C. Egos,
  • Ivy Kim D. Machica

摘要

This paper discusses the development of a mobile application that analyzes and detects Tagalog-English smishing attacks using the Bidirectional Encoder Representations from Transformers (BERT) model integrated into the specified Cross-Industry Standard Process for Data Mining (CRISP-DM) frame-work. The Tagalog-English datasets from Kaggle and the National Telecommunications Commission (NTC) were analyzed using the BERT model, with an accuracy 98. 10% for mBERT and 98.29% for distilBERT.