Blockchain Based Vehicle Authentication Scheme for Vehicular Ad-hoc Networks
Bibliographic record
Abstract
Vehicular Ad Hoc Network (VANET) is a pervasive network, where vehicles communicate with nearby vehicles and infrastructure nodes, such as Road-side unit (RSU). Information sharing among vehicles is an essential component of an intelligent transportation system (ITS), but security and privacy concerns must be taken into consideration. Security of the network can be improved by granting access only to authenticated vehicles and restricting or revoking access for vehicles involved in misbehavior. In this paper, we present a novel blockchain based approach to authenticate vehicles and notify other vehicles about any unauthorized messages in real time. This helps protect other vehicles in the network from making critical decisions based on false or inaccurate information. In the proposed architecture, vehicles communicate with each other using pseudonyms or pseudo IDs and the Blockchain is used to securely maintain the real identity of all vehicles, which can be linked to the pseudo IDs if needed. The goal is to protect privacy or individual vehicles, while still ensuring accountability in case of misbehavior. The performance of the proposed approach is evaluated for different vehicle and attacker densities, and results demonstrate it has lower authentication delay and communication overhead compared to existing approaches.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".