Novel Trust Framework for Vehicular Networks
Bibliographic record
Abstract
Dedicated short range communication is proposed for vehicle to vehicle communications to learn about significant events in the network from neighboring vehicles. However, these neighbors may be malicious and report incorrect events in order to take advantage of the system. The malicious nodes may also provide incorrect recommendations about their peers in order to exert a stronger influence on the receiver's decision. Incorrect information and malicious nodes render the system unreliable for safety and emergency applications. In order to correctly identify the events as well as malicious nodes, a novel trust framework is proposed in this paper that studies all aspects of the trust in connected vehicle (CV) to CV communications. The nodes iteratively learn about the environment from received messages and then update the trust values of their neighbors. Nodes are classified on the basis of their trust values and reported events are also classified as true and false. Nodes advertise their recommendation about trusted and malicious neighbors. The proposed framework allows nodes to identify and filter recommendations from malicious nodes, and to discern true events. The performance of the proposed framework is evaluated experimentally using false and true positive rates, event detection probability and trust computation error. The proposed framework identifies malicious nodes and true events with high probability of more than 0.92 while keeping the trust computation error below 0.03.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| 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".