Zta-Driven Hierarchical Byzantine Consensus for Scalable O-Ran Supply Chain Security
Bibliographic record
Abstract
Open RAN (O-RAN) multi-vendor interoperability continues to grow at a rapid pace, bringing flexibility and innovation but also increasing the attack surface. This makes O-RAN deployments vulnerable to supply chain infiltration and firmware tampering. Blockchain has emerged as a promising solution to provide an immutable and verifiable audit trail of equipment firmware hashes from manufacturing through deployment. However, conventional blockchain designs rely on all-to-all validator communication, which introduces scalability challenges and increases verification latency. To alleviate this issue, in this paper, we propose a scalable, efficient, and lightweight blockchain-enabled consensus framework, called H-BFT, to ensure supply chain security and attestation in O-RAN environments. H-BFT consists of three modules. The first module is a hierarchical consensus and attestation module that maps ZeroTrust Architecture (ZTA) microsegments (i.e., RU/DU/CU zones, near-real-time RIC clusters, and SMO domains) to validator committees. These committees verify local attestation events and produce concise summaries. The second module is a lightweight leader checkpointing module that periodically aggregates crosssegment digests, so that only compact validations become global, reducing communication and latency overhead. The third module is a blockchain-based audit and integrity enforcement module, where segment-level contracts enforce onboarding and integrity guarantees, and a checkpoint manager maintains consistency across the entire network.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".