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.
@article{arxiv.2509.10543,
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},
journal= {arXiv preprint arXiv:2509.10543},
year = {2025}
}
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The 27th International Conference on Artificial Intelligence (ICAI'25)