Secure service discovery protocol for intelligent transport systems
Bibliographic record
Abstract
Intelligent Transportation Systems (ITS) have attracted many researchers that invested a lot of effort to develop many applications mainly for vehicular systems. Existing and investigated applications in vehicular systems are classified in two folds: safety applications and convenience applications. The first type of applications focuses mainly on drivers security and safety on the roads by providing warnings and alerts if there are accidents or disasters. The second type of applications helps drivers and passengers to localize services, get accurate weather or road condition information, get entertainment access, or even interact with surrounding services. Thus, a secure service discovery and communication protocol is mandatory in order to prevent from many attacks and malicious processes in the vehicular system. An efficient service discovery protocol should guarantee a secure discovery and communication in the vehicular system while maintaining the network scalability and a low communication delay. In this paper, we present the possible attacks that can happen at the discovery level and at the routing level. Then, we present our secure service discovery protocol for vehicular systems designed for the convenience type of applications. We prove the correctness of our proposed scheme and we compute its message complexity.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".