Cooperative Message Authentication in Vehicular Cyber-Physical Systems
Bibliographic record
Abstract
The vehicular ad hoc network presents a very complex cyber-physical system with intricate interplay between the physical and cyber domains. In the physical domain, vehicles need to frequently broadcast their geographic information. The safety message broadcasting in an area with a high density of vehicles tends to incur a large data traffic rate that should be properly processed in the cyber domain. In this paper, we address the issue of large computation overhead caused by the safety message authentication. Especially, a cooperative message authentication protocol (CMAP) is developed to alleviate vehicles' computation burden. With CMAP, all the vehicles share their verification results with each other in a cooperative way, so that the number of safety messages that each vehicle needs to verify reduces significantly. Furthermore, we study the verifier selection algorithms for a high detection rate of invalid messages in a practical 2-D road scenario. Another important contribution in this paper is that we develop an analytical model for CMAP and the existing probabilistic verification protocol , considering the hidden terminal impact. Simulation results over a practical map are presented to demonstrate the performance of the proposed CMAP with comparison to the existing method.
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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 | 0.006 |
| 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 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".