Efficient group‐based authentication protocol for location‐based service discovery in intelligent transportation systems
Bibliographic record
Abstract
ABSTRACT Intelligent transportation systems have attracted many researchers. These latter have invested much effort to develop many applications and services mainly for vehicular systems. Services can be classified as safety or convenience services. A service discovery mechanism is needed to permit the discovery and the interaction with the existing services. However, so far security issues for service discovery in vehicular systems have not been widely considered, mainly for the convenience type of applications. Thus, a secure service discovery and communication protocol is necessary to prevent from many attacks and malicious processes in the vehicular system. In this paper, we investigate the possible attacks that can occur during the service discovery and communication processes. Then, we present our proposed group‐based authentication scheme for secure service discovery and communication in vehicular systems. Our proposed scheme is mainly dedicated for the convenience type of applications. We discuss the security requirements achieved by our proposed protocol and we report on its performance evaluation. We prove through our extensive set of simulations that our proposed scheme achieves a high success rate for the secure discovery of services, while maintaining the scalability of the network and low discovery delays. Copyright © 2013 John Wiley & Sons, Ltd.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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".