SH-CRAN
Bibliographic record
Abstract
The heterogeneous cloud radio access network (H-CRAN) has been emerging as a cost-effective solution supporting huge volumes of mobile traffic in the big data era. This paper investigates potential security challenges on H-CRAN and analyzes their likelihoods and difficulty levels. Typically, the security threats in H-CRAN can be categorized into three groups, i.e., security threats towards remote radio heads (RRHs), those towards the radio cloud infrastructure and towards backhaul networks. To overcome challenges made by the security threats, we propose a hierarchical security framework called Secure H-CRAN (SH-CRAN) to protect the H-CRAN system against the potential threats. Specifically, the architecture of SH-CRAN contains three logically independent secure domains (SDs), which are the SDs of radio cloud infrastructure, RRHs and backhauls. The notable merits of SH-CRAN include two aspects: (i) the proposed framework is able to provide security assurance for the evolving H-CRAN system, and (ii) the impacts of any failure are limited in one specific component of H-CRAN. The proposed SH-CRAN can be regarded as the basis of the future security mechanisms of mobile bag data computing.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".