ARGOS: Anomaly Recognition and Guarding through O-RAN Sensing
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866909642435592192 |
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| author | Dimou, Stavros Noubir, Guevara |
| author_facet | Dimou, Stavros Noubir, Guevara |
| contents | Rogue Base Station (RBS) attacks, particularly those exploiting downgrade vulnerabilities, remain a persistent threat as 5G Standalone (SA) deployments are still limited and User Equipment (UE) manufacturers continue to support legacy network connectivity. This work introduces ARGOS, a comprehensive O-RAN compliant Intrusion Detection System (IDS) deployed within the Near Real-Time RIC, designed to detect RBS downgrade attacks in real time, an area previously unexplored within the O-RAN context. The system enhances the 3GPP KPM Service Model to enable richer, UE-level telemetry and features a custom xApp that applies unsupervised Machine Learning models for anomaly detection. Distinctively, the updated KPM Service Model operates on cross-layer features extracted from Modem Layer 1 (ML1) logs and Measurement Reports collected directly from Commercial Off-The-Shelf (COTS) UEs. To evaluate system performance under realistic conditions, a dedicated testbed is implemented using Open5GS, srsRAN, and FlexRIC, and validated against an extensive real-world measurement dataset. Among the evaluated models, the Variational Autoencoder (VAE) achieves the best balance of detection performance and efficiency, reaching 99.5% Accuracy with only 0.6% False Positives and minimal system overhead. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_06916 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ARGOS: Anomaly Recognition and Guarding through O-RAN Sensing Dimou, Stavros Noubir, Guevara Networking and Internet Architecture Cryptography and Security Rogue Base Station (RBS) attacks, particularly those exploiting downgrade vulnerabilities, remain a persistent threat as 5G Standalone (SA) deployments are still limited and User Equipment (UE) manufacturers continue to support legacy network connectivity. This work introduces ARGOS, a comprehensive O-RAN compliant Intrusion Detection System (IDS) deployed within the Near Real-Time RIC, designed to detect RBS downgrade attacks in real time, an area previously unexplored within the O-RAN context. The system enhances the 3GPP KPM Service Model to enable richer, UE-level telemetry and features a custom xApp that applies unsupervised Machine Learning models for anomaly detection. Distinctively, the updated KPM Service Model operates on cross-layer features extracted from Modem Layer 1 (ML1) logs and Measurement Reports collected directly from Commercial Off-The-Shelf (COTS) UEs. To evaluate system performance under realistic conditions, a dedicated testbed is implemented using Open5GS, srsRAN, and FlexRIC, and validated against an extensive real-world measurement dataset. Among the evaluated models, the Variational Autoencoder (VAE) achieves the best balance of detection performance and efficiency, reaching 99.5% Accuracy with only 0.6% False Positives and minimal system overhead. |
| title | ARGOS: Anomaly Recognition and Guarding through O-RAN Sensing |
| topic | Networking and Internet Architecture Cryptography and Security |
| url | https://arxiv.org/abs/2506.06916 |