5G Vehicle-to-Everything Services: Gearing Up for Security and Privacy
Bibliographic record
Abstract
5G is emerging to serve as a platform to support networking connections for sensors and vehicles on roads and provide vehicle-to-everything (V2X) services to drivers and pedestrians. 5G V2X communication brings tremendous benefits to us, including improved safety, high reliability, large communication coverage, and low service latency. On the other hand, due to ubiquitous network connectivity, it also presents serious trust, security, and privacy issues toward vehicles, which may impede the success of 5G V2X. In this article, we present a comprehensive survey on the security of 5G V2X services. Specifically, we first review the architecture and the use cases of 5G V2X. We also study a series of trust, security, and privacy issues in 5G V2X services and discuss the potential attacks on trust, security, and privacy in 5G V2X. Then, we offer an in-depth analysis of the state-of-the-art strategies for securing 5G V2X services and elaborate on how to achieve the trust, security, or privacy protection in each strategy. Finally, by pointing out several future research directions, it is expected to draw more attention and efforts into the emerging 5G V2X services.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".