| Full text | |
| Author(s): |
Xavier, Bruno Missi
;
Dzaferagic, Merim
;
Collins, Diarmuid
;
Comarela, Giovanni
;
Martinello, Magnos
;
Ruffini, Marco
Total Authors: 6
|
| Document type: | Journal article |
| Source: | ICC 2023-IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS; v. N/A, p. 6-pg., 2023-01-01. |
| Abstract | |
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) | |
| FAPESP's process: | 20/05174-0 - SAWI - Savvy Access through Worldwide Internet |
| Grantee: | Epaminondas Aguiar de Sousa Junior |
| Support Opportunities: | Research Grants - Innovative Research in Small Business - PIPE |
| FAPESP's process: | 20/05182-3 - PORVIR-5G: programability, orchestration and virtualization in 5G networks |
| Grantee: | José Marcos Silva Nogueira |
| Support Opportunities: | Research Projects - Thematic Grants |