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Distributed Denial-of-Service (DDoS) attacks remain a serious threat to online infrastructure, often bypassing detection by altering traffic in subtle ways. We present a method using hive-plot sequences of network data and a 3D convolutional neural network (3D CNN) to classify DDoS traffic with high accuracy. Our system relies on three main ideas: (1) using spatio-temporal hive-plot encodings to set a pattern-recognition baseline, (2) applying adversarial training with FGSM and PGD alongside spatial noise and image shifts, and (3) analyzing frame-wise predictions to find early signals. On a benchmark dataset, our method lifts adversarial accuracy from 50–55% to over 93% while maintaining clean-sample performance. Frames 3–4 offer strong predictive signals, showing early-stage classification is possible.

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Robust DDoS-Attack Classification with 3D CNNs Against Adversarial Methods

  • 会议论文

  • 发表日期：

    2026年

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  • Landon Bragg,
  • Nathan Dorsey,
  • Josh Prior,
  • John Ajit,
  • Ben Kim,
  • Nate Willis,
  • Pablo Rivas

摘要

Distributed Denial-of-Service (DDoS) attacks remain a serious threat to online infrastructure, often bypassing detection by altering traffic in subtle ways. We present a method using hive-plot sequences of network data and a 3D convolutional neural network (3D CNN) to classify DDoS traffic with high accuracy. Our system relies on three main ideas: (1) using spatio-temporal hive-plot encodings to set a pattern-recognition baseline, (2) applying adversarial training with FGSM and PGD alongside spatial noise and image shifts, and (3) analyzing frame-wise predictions to find early signals. On a benchmark dataset, our method lifts adversarial accuracy from 50–55% to over 93% while maintaining clean-sample performance. Frames 3–4 offer strong predictive signals, showing early-stage classification is possible.