Zero Trust Architecture for Enhanced Security in O-RAN Service Management and Orchestration (SMO)
Bibliographic record
Abstract
The introduction of the Service Management and Orchestration (SMO) framework presents a transformative approach to managing Open Radio Access Network (O-RAN) by integrating essential functions such as RAN configuration, inventory management, and Fault, Configuration, Accounting, Performance, and Security (FCAPS) management. Despite its capabilities and the inclusion of rApps (applications supporting RAN services) the security of the SMO remains a critical concern, particularly due to its interactions with external networks data sources. This paper addresses the security challenges inherent to the SMO, emphasizing the need for a Zero Trust Architecture (ZTA) to safeguard against cyberattacks. Our research proposes a novel security approach leveraging continuous monitoring and dynamic policy enforcement to enhance the SMO 's security posture. By adopting ZTA principles, we aim to mitigate risks associated with Advanced Persistent Threats (APT), ensuring the robustness and reliability of O-RAN infrastructure and services. To do so, we illustrate our proposed security solution called Zero Trust SMO Service (ZT-SMOS), including its possible way forward for implementation and its advantages. At the end, this research highlights the importance of securing the SMO to maintain optimal network performance and service quality for users within the evolving landscape of 5G mobile networks.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".