Security Enhancing Method in Vehicular Networks by Exploiting the Accurate Traffic Flow Prediction
Bibliographic record
Abstract
In recent years, to improve the transportation system's efficiency, relying on the development of vehicular wireless communication technology and corresponding in-vehicle sensor technology, intelligent transportation systems have become the focus of attention in academia and industry. This is because, by improving the vehicle's ability to perceive the surrounding traffic environment through the exchange of information between vehicles, the vehicle's safety can be effectively improved, which reduces the occurrence of traffic accidents, in turn improving the efficiency of the transportation system. However, while data exchange brings convenience, similar to the other data communication applications, communication security issues inevitably enter the Internet-of-Vehicles environment since the vehicle is no longer an isolated individual. Accordingly, in this paper, we will propose a data transmission security improvement method based on accurate traffic flow prediction for addressing a specific data theft problem. Briefly, our method can detect the fraud vehicle that spread fake traffic information to increase the possibility that it may be selected as a relay node, thereby increasing its theft of the data of related users who use it as a relay node. Intensive simulation experiments are conducted to evaluate and prove the efficiency of our proposed work.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".