Trust-Based Cooperative Game Model for Secure Collaboration in the Internet of Vehicles
Bibliographic record
Abstract
The Internet of Vehicles (IoV) is an emerging computing paradigm that delivers intelligent transportation services. In an IoV system, the legitimacy, reliability, and accuracy of circulating data have a direct impact on decisions and operations, and eventually, public safety and economy. In this paper, we design a decentralized secure collaboration scheme that protects the vehicles in the IoV environment against the attacks on data integrity. First, the trustworthiness of the vehicles is computed based on their experience acquired from direct interactions using a Bayesian inference model. Then, based on the established trust relationships between the vehicles, we present a vehicular coalition formation approach that incorporates a hedonic cooperative game model, which aims at preventing malicious or faulty vehicles from joining benign vehicular collaborative communities. Simulation results show that the proposed scheme is highly resilient to data alteration and corruption attacks. The scheme also demonstrates to be scalable, and will ultimately allow the IoV entities and platform to derive optimal operative decisions on the fly.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".