An open testbed for O-RAN experimentation with AI-enabled control and monitoring
The proliferation of open radio access networks (O-RAN) in modern 5G systems has ushered in enhanced flexibility and efficiency, but it has also introduced novel security challenges. In response, this paper presents a novel AI-based anomaly detection framework tailored for O-RAN networks operating i...
| Autores: | , , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Centre Tecnològic de Telecomunicacions de Catalunya (CTTC) |
| Repositorio: | r-CTTC. Repositorio Institucional Producción Científica del Centre Tecnològic de Telecomunicacions de Catalunya (CTTC) |
| OAI Identifier: | oai:cttc.fundanetsuite.com:p8756 |
| Acceso en línea: | https://cttc.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=8756 |
| Access Level: | acceso abierto |
| Palabra clave: | O-RAN 5G Anomaly detection Principal component analysis Deep neural network Cybersecurity Open-source testbed Network resilience |
| Sumario: | The proliferation of open radio access networks (O-RAN) in modern 5G systems has ushered in enhanced flexibility and efficiency, but it has also introduced novel security challenges. In response, this paper presents a novel AI-based anomaly detection framework tailored for O-RAN networks operating in 5G environments. By employing principal component analysis for dimensionality reduction and a deep neural network for classification, the proposed system efficiently processes large-scale 5G traffic data while achieving high detection accuracy and low latency. Experimental evaluation on an open-source testbed with realistic cellular traffic demonstrates rapid convergence, with both training and validation accuracy values approaching 100% and effective detection of anomalies introduced via user equipment identifier swaps. The testbed processed over 300,000 traffic samples with 31 distinct network features, emulating 8 unique user equipment profiles under diverse radio conditions. Under adversarial scenarios, such as identity-swapping attacks, the system identified anomalous behavior with detection rates exceeding 40%, while maintaining a near-zero false positive rate on clean traffic. These results underscore the testbed's capability to simulate complex 5G environments and the framework's ability to deliver highly accurate, low-latency, and scalable anomaly detection. Overall, this work highlights the potential of advanced AI techniques to significantly enhance the security and resilience of modern wireless communication networks. |
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