| Full text | |
| Author(s): |
Xavier, Bruno Missi
;
Dzaferagic, Merim
;
Vila, Irene
;
Martinello, Magnos
;
Ruffini, Marco
Total Authors: 5
|
| Document type: | Journal article |
| Source: | ICC 2024 - IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS; v. N/A, p. 6-pg., 2024-01-01. |
| Abstract | |
In the fight against cyber attacks, Network Softwarization (NS) is a flexible and adaptable shield, using advanced software to spot malicious activity in regular network traffic. However, the availability of comprehensive datasets for mobile networks, which are fundamental for the development of Machine Learning (ML) solutions for attack detection near their source, is still limited. Cross-Domain Artificial Intelligence (AI) can be the key to address this, although its application in Open Radio Access Network (O-RAN) is still at its infancy. To address these challenges, we deployed an end-to-end O-RAN network, that was used to collect data from the RAN and the transport network. These datasets allow us to combine the knowledge from an in-network ML traffic classifier for attack detection to bolster the training of an ML-based traffic classifier specifically tailored for the RAN. Our results demonstrate the potential of the proposed approach, achieving an accuracy rate of 93%. This approach not only bridges critical gaps in mobile network security but also showcases the potential of cross-domain AI in enhancing the efficacy of network security measures. (AU) | |
| 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 |
| 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 |