Daniel Lazkani Feferman
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Daniel Lazkani Feferman has a total of 14 co-authors. Here are the top co-authors by number of shared publications:
- Francisco Hugo Costa Neto
Centro de Pesquisa e Desenvolvimento em Telecomunicaes (CPQD), Campinas, Brazil
2 shared publications - Eduardo Ebeling de Almeida
CPqD, Campinas, Brazil
1 shared publication - Eduardo Melão
CPqD, Campinas, Brazil
1 shared publication - Erika Costa Alves
CPQD
1 shared publication - Fuad Mousse Abinader Junior
CPQD
1 shared publication - João Paulo Henriques Sales de Lima
G and 6G Innovation Centre, Institute for Communication Systems, University of Surrey, Guildford, U.K.
1 shared publication
Latest Publications
- Open Access
- 4 December 2024
- Published by Association for Computing Machinery (ACM)
- p. 2306-2311
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.
- 4 December 2024
- Published by Association for Computing Machinery (ACM)
- p. 2306-2311
Open AccessEvaluation 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.
- Open Access
- 4 December 2024
- Published by Association for Computing Machinery (ACM)
- p. 1850-1857
Performance Analysis of Outdoor Open RAN Deployments in Long-Haul 5G Networks using the OpenRAN@Brasil Testbed
This paper presents the OpenRAN@Brasil testbed, designed to address the challenges of Open RAN deployments in Brazil. It is composed by commercial off-the-shelf 5G Open RAN components, distributed over two sites, and interconnected by a 400 km midhaul link. We use this to evaluate end-to-end network performance (latency and throughput) under diverse signal conditions. Results show that the long-haul link increases latency to up to 28 ms, degrading downlink throughput by up to 400 Mbps, while the lower uplink throughput is less affected. These findings highlight the importance of strategic RAN component placement and robust signal quality for optimizing Open RAN performance in challenging environments, particularly within the Brazilian ecosystem.
- 4 December 2024
- Published by Association for Computing Machinery (ACM)
- p. 1850-1857
Open AccessPerformance Analysis of Outdoor Open RAN Deployments in Long-Haul 5G Networks using the OpenRAN@Brasil Testbed
This paper presents the OpenRAN@Brasil testbed, designed to address the challenges of Open RAN deployments in Brazil. It is composed by commercial off-the-shelf 5G Open RAN components, distributed over two sites, and interconnected by a 400 km midhaul link. We use this to evaluate end-to-end network performance (latency and throughput) under diverse signal conditions. Results show that the long-haul link increases latency to up to 28 ms, degrading downlink throughput by up to 400 Mbps, while the lower uplink throughput is less affected. These findings highlight the importance of strategic RAN component placement and robust signal quality for optimizing Open RAN performance in challenging environments, particularly within the Brazilian ecosystem.