High-Throughput and Low-Latency DrDoS Detection Using Quantized ONNX Models

Eren S., Gültekin A., Özkan Ö., ÖZÇELİK İ.

Symmetry, cilt.18, sa.7, 2026 (SCI-Expanded, Scopus)

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 18 Sayı: 7
  • Basım Tarihi: 2026
  • Doi Numarası: 10.3390/sym18071187
  • Dergi Adı: Symmetry
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, INSPEC, zbMATH, Academic Search Ultimate (EBSCO), Materials Science & Engineering Collection (ProQuest), Technology Collection (ProQuest)
  • Anahtar Kelimeler: DDoS, deep learning, DNS amplification, DrDoS, intrusion detection, MLP, network security, ONNX, quantization, real-time detection
  • Eskişehir Osmangazi Üniversitesi Adresli: Evet

Özet

Distributed Reflection Denial-of-Service (DrDoS) attacks, such as DNS Amplification, exploit inherent asymmetries in standard network protocols to generate devastating traffic volumes. To restore defense-side balance against these asymmetric threats, we present a comprehensive, systematic comparison of ONNX compilation and quantization configurations across Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), and Gated Recurrent Unit (GRU) architectures evaluated on a unified reference platform. To achieve this, our evaluation analyzes training durations, throughput scalability, and sample latency across varying batch sizes to align with operational network environments. We demonstrate that while small batch sizes suffer from data transfer overhead, increasing batch configurations significantly accelerates GPU throughput, particularly for statically quantized ONNX models. Additionally, training durations exhibit an inverse scaling relationship with batch size, yielding massive temporal savings as workloads expand. Furthermore, larger batch sizes effectively amortize fixed execution costs across thousands of samples, reducing average per-sample latency. Ultimately, this systematic evaluation provides a deployment blueprint for highly efficient intrusion detection engines capable of neutralizing asymmetric network threats at line-rate.