ARGOS: Anomaly Recognition and Guarding through O-RAN Sensing

• URL: http://arxiv.org/abs/2506.06916v1
• Date: Sat, 07 Jun 2025 20:32:23 GMT
• Title: ARGOS: Anomaly Recognition and Guarding through O-RAN Sensing
• Authors: Stavros Dimou, Guevara Noubir,
• Abstract summary: Rogue Base Station (RBS) attacks, particularly those exploiting downgrade vulnerabilities, remain a persistent threat.<n>This work introduces ARGOS, a comprehensive O-RAN compliant Intrusion Detection System (IDS) deployed within the Near Real-Time RIC.<n>ARGOS detects RBS downgrade attacks in real time, an area previously unexplored within the O-RAN context.
• Score: 3.018691733760647
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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