Evaluation of the Latency of Machine Learning Random Access DDoS Detection in Open RAN
Authors:
- Paulo Ricardo Branco da SilvaPSPaulo Ricardo Branco da Silva
- João Paulo Henriques Sales de LimaJLJoão Paulo Henriques Sales de Lima
- Erika Costa AlvesEAErika Costa Alves
- William Sanchez FarfanWFWilliam Sanchez Farfan
- Victor Aguiar CoutinhoVCVictor Aguiar Coutinho
- Thomas William Do Prado PaivaTPThomas William Do Prado Paiva
- Daniel Lazkani FefermanDFDaniel Lazkani Feferman
- Francisco Hugo Costa NetoFNFrancisco Hugo Costa Neto
Open Access
- 4 December 2024
- conference paper
- Published by Association for Computing Machinery (ACM)
- p. 2306-2311
Abstract
A testbed with open-source tools and commercial equipment is designed to investigate the timing of ML-based xApp operations in DDoS attack detection during the Random Access procedure. Results show high-precision and high-recall ML classifiers operating faster than the Medium Access Control (MAC) layer's Contention Resolution timer. For classifiers with over 97% precision and recall, XGBoost has the fastest execution time and the narrowest estimated run time probability density function.Keywords
Funding Information
- Empresa Brasileira de Pesquisa e Inovação Industrial/Ministério da Ciência, Tecnologia, Inovações e Comunicações (51/2023)
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