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In modern communication networks, spam detection remains a critical challenge due to the evolving nature of unsolicited content. This study presents a deep learning approach using a Bidirectional Long Short-Term Memory (Bi-LSTM) model for enhanced spam classification. The model is trained and evaluated on a benchmark dataset comprising 5,574 labeled SMS messages, including both spam and ham texts. Leveraging Bi-LSTM's ability to capture sequential dependencies, the proposed model achieves superior performance with an accuracy of 98%, precision of 98%, recall of 1.00, and F1-score of 0.99. These results demonstrate the effectiveness of the approach in improving spam detection accuracy over traditional methods.

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A Deep Learning Approach for Accurate Spam Detection in Text

  • 会议论文

  • 发表日期：

    2026年

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  • Sumeera BiBi,
  • Asad Khattak,
  • Hayat Ullah,
  • Muhammad Usama Asghar,
  • Muhammad Zubair Asghar,
  • Wasim Abbas

摘要

In modern communication networks, spam detection remains a critical challenge due to the evolving nature of unsolicited content. This study presents a deep learning approach using a Bidirectional Long Short-Term Memory (Bi-LSTM) model for enhanced spam classification. The model is trained and evaluated on a benchmark dataset comprising 5,574 labeled SMS messages, including both spam and ham texts. Leveraging Bi-LSTM's ability to capture sequential dependencies, the proposed model achieves superior performance with an accuracy of 98%, precision of 98%, recall of 1.00, and F1-score of 0.99. These results demonstrate the effectiveness of the approach in improving spam detection accuracy over traditional methods.