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

• URL: http://arxiv.org/abs/2509.10543v1
• Date: Sun, 07 Sep 2025 00:20:32 GMT
• Title: Robust DDoS-Attack Classification with 3D CNNs Against Adversarial Methods
• Authors: Landon Bragg, Nathan Dorsey, Josh Prior, John Ajit, Ben Kim, Nate Willis, Pablo Rivas,
• Abstract summary: We present a method using hive-plot network data and a 3D convolutional neural network (3D CNN) to classify DDoS traffic with high accuracy.<n>On a benchmark dataset, our method lifts adversarial accuracy from 50-55% to over 93% while maintaining clean-sample performance.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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