| Texto completo | |
| Autor(es): |
Xavier, Bruno Missi
;
Dzaferagic, Merim
;
Collins, Diarmuid
;
Comarela, Giovanni
;
Martinello, Magnos
;
Ruffini, Marco
Número total de Autores: 6
|
| Tipo de documento: | Artigo Científico |
| Fonte: | ICC 2023-IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS; v. N/A, p. 6-pg., 2023-01-01. |
| Resumo | |
We design and demonstrate a method for early detection of Denial-of-Service attacks. The proposed approach takes advantage of the OpenRAN framework to collect measurements from the air interface (for attack detection) and to dynamically control the operation of the Radio Access Network (RAN). For that purpose, we developed our near-Real Time (RT) RAN Intelligent Controller (RIC) interface. We apply and analyze a wide range of Machine Learning algorithms to data traffic analysis that satisfy the accuracy and latency requirements set by the near-RT RIC. Our results show that the proposed framework is able to correctly classify genuine vs. malicious traffic with high accuracy (i.e., 95%) in a realistic testbed environment, allowing us to detect attacks already at the Distributed Unit (DU), before malicious traffic even enters the Centralized Unit (CU). (AU) | |
| Processo FAPESP: | 20/05174-0 - SAWI - Savvy Access through Worldwide Internet |
| Beneficiário: | Epaminondas Aguiar de Sousa Junior |
| Modalidade de apoio: | Auxílio à Pesquisa - Pesquisa Inovativa em Pequenas Empresas - PIPE |
| Processo FAPESP: | 20/05182-3 - PORVIR-5G: programabilidade, orquestração e virtualização em redes 5G |
| Beneficiário: | José Marcos Silva Nogueira |
| Modalidade de apoio: | Auxílio à Pesquisa - Temático |