A blockchain-based lightweight authentication and key agreement scheme for internet of vehicles
Bibliographic record
Abstract
The Internet of Vehicles is deployed in an open environment, and protecting its security and data privacy is the challenge. Carrying out the Internet of Vehicles secure authentication before interaction of information is an important part of ensuring the security foundation. Therefore, this article designs a safe and reliable Internet of Vehicles authentication and key agreement schemeassisted by blockchain. This article uses a multi-TA network model to improve the efficiency of authentication. Because of the rapid movement of vehicles, it will continue to appear cross-RSU and TA certification. Considering the disadvantages of most centralised authentication protocols using a single TA, this paper uses the multi-TA model to improve the efficiency of authentication. By usingblockchain technology to store the authentication information of vehicles, the cross-domain authentication of vehicles and the protection of user privacy information can be well realised. At the same time, in order to reduce the time of vehicle authentication, this scheme uses a lightweight calculation operation to complete the whole process of authentication. Through security analysis and results of Proverif simulation, the security of the solution is well proved, our scheme can resist various common attacks. Compared with some existing Internet of Vehicles security authentication protocols, the proposed scheme has a lower cost of computation, communication, and storage.
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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) | 0.000 | 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 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".