Integrating Zero Trust Architecture in O-RAN: A Comprehensive Survey and Analysis
Bibliographic record
Abstract
Open Radio Access Network (O-RAN) is a new approach to mobile networks that disaggregates the network architecture into multi-vendor interoperable physical and software-defined network components connected through standardized open interfaces. This architecture enables deploying solutions on cloud-native platforms and boosting Artificial Intelligence(AI)-driven automation for network optimization. Despite the benefits of O-RAN’s heterogeneous and multi-vendor architecture, this approach inevitably enlarges the attack surface and introduces additional trust boundaries, which imminently threaten the uniformity of network performance. This also justifies the necessity of Zero Trust Architecture (ZTA) principles as a countermeasure, securing all network components, interfaces, and data flows. This survey shows how ZTA tenets can be integrated into O-RAN settings, contributing in three key areas: (1) a novel ZTA-to-O-RAN mapping model that explicitly places Policy Engine (PE), Policy Administrator (PA), and Policy Enforcement Points (PEPs) across RIC layers (Non-RT/Near-RT RIC), standardized interfaces (A1/E2/O1/O2), and disaggregated RAN functions (O-DU/O-CU/O-RU); (2) a Risk-Adaptive Access Control (RAdAC) framework that dynamically modulates verification depth based on contextual risk; and (3) integration of blockchain-based decentralized identity management with smart-contract-driven authorization for xApp/rApp lifecycle security.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".