## 4 Threat Model for O-RAN Optimization
### Key Background Information for O-RAN Security and Adversarial ML Research
Threats include side-channel attacks, privilege escalation, interface hijacking, and the manipulation of telemetry/control flows [27; 46; 47; 50]. **Cross-Layer Attack Chains:** Some works explicitly model adversaries who can coordinate attacks across both the ML and system layers, for example, by combining data poisoning with the exploitation of open interfaces or system-level vulnerabilities [27; 46; 48]. * **New Datasets, Tools, and Methodologies** Several references contribute novel testbeds, methodologies, or analytical frameworks for empirical and conceptual studies in this space. * _Experimental O-RAN Testbeds:_ The deployment of near-RT RICs, xApps, and related components in either laboratory or over-the-air LTE/5G setups to measure the tangible impacts of attacks and defenses [46; 48; 49; 52; 54]. * _Measurement Metrics:_ Metrics used include classification accuracy, interference mitigation, coverage rates (e.g., fifth-percentile user throughput), and IDS detection latency [27; 48; 49; 52; 54]. * _Novel Attack/Defense Implementations:_ The prototyping of malicious xApps and the evaluation of distillation-based robust models [48]; attack variants, including \"policy infiltration\" against DRL agents [52]; and sophisticated evasion noise injections [27]. IDS integration within the near-RT RIC for real-time model poisoning detection is also explored [54]. * _Analytical and Taxonomic Tools:_ Security ontologies and attack surface maps for detailed threat modeling [47; 50], including graph-based representations mapping system components and ML pipelines to illuminate cross-layer exploit paths [50]. Our threat modeling approach leverages two foundational datasets that are specifically designed to support O-RAN security analysis. The first dataset, DAWN [11], provides rich 5G and 6G traffic traces that incorporate adversarial attack scenarios featuring 15% poisoned samples. It also supports multi-slice configurations, including enhanced Mobile Broadband (eMBB), Ultra-Reliable Low-Latency Communications (URLLC), and massive Machine-Type Communications (mMTCs). Additionally, DAWN offers 1 ms resolution temporal features such as Physical Resource Block (PRB) usage, Reference Signal Received Power (RSRP), and Hybrid Automatic Repeat Request (HARQ) events. Complementing DAWN, the OpenRAN Gym dataset [19] supplies O-RAN-specific metrics, including behavioral logs from xApps and rApps, DistributedUnit (DU) energy profiles ranging from 0 to 500 watts, and Near-Real-Time control loop latencies. The integration of these datasets enables three critical methodological advances in our study. First, they support experimental validation by allowing the reproduction of 38 known O-RAN vulnerabilities using DAWN's adversarial attack traces. Second, they facilitate metric standardization by aligning anomaly scores with the control intervals defined by OpenRAN Gym, which range from 10 ms to 1 s. Third, they enable attack surface mapping by correlating DAWN's high-resolution temporal features with telemetry data collected from xApps within OpenRAN Gym, providing a comprehensive view of cross-layer vulnerabilities and behaviors in the O-RAN ecosystem. 5. **Importance and Application Context** The interplay between open, cloud-native telecom architectures and advanced ML workflows means O-RAN deployments face both traditional cyber threats and ML-specific adversarial attacks. Defending these hybrid systems requires * A deep understanding of how vulnerabilities in RAN interfaces, software containers, and ML pipelines can be combined in coordinated attacks. * Empirical studies to validate the real-world feasibility and impact of attacks and defenses. * The design of practical, deployable mitigation strategies that respect O-RAN's latency and performance constraints. Research in this space informs both immediate operational risk management for current 5G O-RAN deployments and best-practice design for future network generations (6G and beyond).