## 7 Results and Analysis
### Future Work to Enhance Dataset Reliability  
Looking ahead, enhancing the representativeness and reliability of the datasets and emulation tools will be critical for advancing O-RAN security research. Future efforts  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Limitation** & **Impact on Results** & **Mitigation Strategy** \\ \hline
**DAWN Dataset:** Limited attack scenarios (e.g., 15\% data poisoning, signaling storms); excludes multi-vector and zero-day exploits & Inflated perceived robustness due to omission of cross-layer and sophisticated attack vectors; underrepresentation of real-world traffic burstiness and spatial variability & Injection of synthetic attacks (e.g., gradient inversion, adversarial E2/A1 examples) into DAWN traces; Monte Carlo simulations under stochastic URLLC traffic \\ \hline
**DAWN Dataset:** Synthetic traffic with limited realism & Underestimated the impact of user behavior anomalies in dense urban or emergency scenarios & Augmented dataset with stress scenarios simulating traffic surges and edge cases \\ \hline
**OpenRAN Gym:** Abstraction of hardware-specific nuances (e.g., vendor-dependent latencies, thermal throttling) & Understated latency penalties and energy overheads in near-RT control loops; optimistic estimates of cross-domain interoperability & Hardware-in-the-loop experiments on physical testbed (commercial O-RUs, near-RT RIC); revealed 5–8\% energy savings deviation \\ \hline
**OpenRAN Gym:** Homogeneous xApp assumptions; lacks heterogeneity of real networks & Overestimation of scalability and interoperability in diverse vendor environments & Integration of real-world telemetry from field-deployed O-RAN systems to enhance emulation realism \\ \hline
**General:** Lack of open, community-driven datasets for adversarial O-RAN evaluation & Limited cross-validation and reproducibility across research efforts & Proposal of open repository for anonymized traces and adversarial payloads for community-driven benchmarking \\ \hline \hline \end{tabular}
\end{table}
Table 15: Summary of dataset and emulation limitations, impacts, and mitigation strategies.  
should prioritize expanding the threat coverage by collaborating with industry consortia, such as the O-RAN Alliance, to curate attack traces derived from real-world O-RAN deployments. This collaboration would facilitate the inclusion of emerging AI-driven threats, such as adversarial policy manipulation within xApps or orchestrated attacks targeting closed-loop control systems. Moreover, OpenRAN Gym should be augmented with hardware-in-the-loop capabilities, integrating telemetry streams from field-deployed O-RAN systems to better capture vendor-specific performance deviations, thermal constraints, and other real-world artifacts currently overlooked by the emulation framework. Finally, establishing an open repository for anonymized network traces and adversarial payloads would enable the broader research community to perform cross-validation of security frameworks across diverse operational contexts and deployment scenarios. These initiatives would not only address the current methodological limitations but also foster reproducibility and realism, ensuring that future evaluations are reflective of the evolving threat landscape and architectural complexity anticipated in 6G O-RAN systems.