Securing O-RAN Equipment Using Blockchain-Based Supply Chain Verification
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The Open Radio Access Network (O-RAN) architecture has enabled the integration of multi-vendor equipment, yielding a significant enhancement in the flexibility and interoperability of telecommunications networks. However, this openness has also introduced new security vulnerabilities, particularly in supply chain integrity. Malicious actors may exploit weaknesses at various stages of production, distribution, or integration, leading to critical threats such as data tampering, unauthorized access, and denial-of-service (DOS) attacks. To address these challenges, this paper proposes a novel blockchain-based framework designed to secure the O-RAN supply chain. The proposed solution leverages a private permissioned blockchain ledger and cryptographic firmware authentication to ensure the integrity and authenticity of network equipment throughout its lifecycle. Specifically, the framework consists of: (1) a decentralized architecture integrating blockchain network components, equipment node validators, and secure firmware authentication mechanisms; and (2) a consensus-based verification model to enhance trust and transparency within the supply chain. To the best of our knowledge, this is one of the first approaches to use blockchain for O-RAN supply chain security, and also addressing emerging security threats in a scalable and tamper-resistant manner. Experimental validation and security assessments demonstrate the effectiveness of the proposed framework in mitigating supply chain risks, making it a promising solution for ensuring trust and robustness in next-generation O-RAN ecosystems.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it