RSU authentication by aggregation in VANET using an interaction zone
Bibliographic record
Abstract
Vehicular ad-hoc network (VANET) is an innovation that will change our vision of road traffic, it will improve the safety and the efficiently of transport. VANET represent a target for attacks that can cause human and material losses. This, highlights the need for a robust security system, who must secure effectively communications between vehicles and the different network's entities, but also, it must guarantee availability and fluidity in the transmission. Specifically, in transmission of messages from Road Side Unit (RSU) to vehicle (R2V), our security aims to ensure identification, authentication, non-repudiation and integrity for the RSU. An aggregation of identification will be achieved by multiples RSU and without asking for the intervention of a trusted third party. In this paper, we proposed a security schema to firstly ensure identification for RSU by an Elliptic Curve Diffie-Hellman (ECDH) algorithm where the vehicle confirms that the two neighbours RSU have the same shared secret, then secondly the vehicle authenticates the message beforehand signing, using Elliptic Curve Digital Signature Algorithm (ECDSA). To simulate the vehicle scenario, we used the OMNET++, SUMO and VEINS combined environment, and we integrated on it the Crypto++ library to achieve the security requirement. Our proposed model ensures stronger security.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".