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As online services continue to grow, Distributed Denial of Service (DDoS) attacks pose significant threats to network systems. While Machine Learning (ML) techniques have increased adaptability in DDoS detection, they still face challenges in capturing complex patterns in high-dimensional or non-linear network data. Deep Learning (DL) models, such as Multilayer Perceptrons (MLP), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) networks, have advanced detection capabilities but encounter limitations when dealing with spatial and temporal data, sequential information, and computational efficiency. To address these challenges, this study proposes the use of the Transformer model for DDoS detection, enhancing its ability to identify intricate patterns in network traffic. Additionally, we introduce a novel packet-based data preprocessing method that preserves detailed packet-level information while maintaining a comprehensive view of data flows. Together, these contributions aim to significantly improve the accuracy and performance of DDoS detection systems.

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A Novel Packet-Based Preprocessing Approach for Transformer Models to Enhance DDoS Detection Accuracy

  • 会议论文

  • 发表日期：

    2026年

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  • Lan Le Duc,
  • Phu Nguyen Phan Hai,
  • Trang Hoang

摘要

As online services continue to grow, Distributed Denial of Service (DDoS) attacks pose significant threats to network systems. While Machine Learning (ML) techniques have increased adaptability in DDoS detection, they still face challenges in capturing complex patterns in high-dimensional or non-linear network data. Deep Learning (DL) models, such as Multilayer Perceptrons (MLP), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) networks, have advanced detection capabilities but encounter limitations when dealing with spatial and temporal data, sequential information, and computational efficiency. To address these challenges, this study proposes the use of the Transformer model for DDoS detection, enhancing its ability to identify intricate patterns in network traffic. Additionally, we introduce a novel packet-based data preprocessing method that preserves detailed packet-level information while maintaining a comprehensive view of data flows. Together, these contributions aim to significantly improve the accuracy and performance of DDoS detection systems.