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

Landon Bragg, Nathan Dorsey, Josh Prior, John Ajit, Ben Kim +2 more
9/7/2025
cs.CRcs.AIcs.LG

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Abstract

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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Cite this paper

@article{bragg2025robust,
  title  = {Robust DDoS-Attack Classification with 3D CNNs Against Adversarial Methods},
  author = {Landon Bragg and Nathan Dorsey and Josh Prior and John Ajit and Ben Kim and Nate Willis and Pablo Rivas},
  year   = {2025},
  eprint = {2509.10543},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2509.10543v1}
}

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