A trust management model in internet of vehicles
Bibliographic record
Abstract
The Internet of Things (IoT) is one of the most evolving technologies, which has a major impact on our daily life. Almost all new devices will have a feature to be connected and controlled over the Internet. Several applications are utilizing IoT to enhance routine processes and actions efficiently. The Internet of Vehicles (IoV) evolved from IoT, where vehicles communicate with each other or with other objects to have a better transportation environment to reduce the number of accidents and save people’s lives. IoV is considered new fields that need security requirements including confidentiality, integrity, availability, authentication, and trust. Trust management technique is used to validate entities behaviors automatically against well-defined policies. The major categories of trust model in IoV are based on entity, data, or a combination of both. This paper proposes a trust model which is based on a combination of entity and data to define the trust of vehicles and utilize the public key infrastructure to distribute certificates to vehicles. Based on certificate validation, messages will be trusted and accepted. This model has been tested across different simulation scenarios which showed that the proposed model detected malicious vehicles and trusted vehicles did not accept their messages.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".