Evaluation of the Latency of Machine Learning Random Access DDoS Detection in Open RAN
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.
Authors
- Daniel Lazkani Feferman (ORCID: https://orcid.org/0000-0002-6481-5116)
- Thomas William do Prado Paiva (ORCID: https://orcid.org/0009-0004-3131-4974)
- Paulo Ricardo Branco da Silva (ORCID: https://orcid.org/0000-0002-4599-6345)
- William Sanchez Farfan (ORCID: https://orcid.org/0000-0003-1862-3567)
- João P. S. H. Lima (ORCID: https://orcid.org/0000-0002-2223-556X)
- Francisco Hugo Costa Neto (ORCID: https://orcid.org/0000-0002-3194-275X)
- Erika Costa Alves (ORCID: https://orcid.org/0009-0005-2498-4715)
- Victor Aguiar Coutinho
Institutions
- Centre for Research and Development in Telecommunications (Brazil) (BR)
Publication Details
- Published
- 2024-12-04
- DOI
- https://doi.org/10.1145/3636534.3701546
- Citations
- 2
- Primary Topic
- Network Security and Intrusion Detection
- Type
- article
- Field-Weighted Citation Impact
- 0.70