Bibliographic record
Abstract
A Vehicular Ad Hoc Network (VANET) technology is becoming one of the most advanced technologies; however, the major barrier it faces is to keep the information secure and to improve its confidentiality. Recently, there have been some methods proposed to keep the information safe and implement the reliable platform for information transmission. However, based on the attacks varieties, and the way they are changing their approach, makes the system still insecure against some certain attacks. To overcome the vulnerability under the eavesdropping attack, we propose a solution to minimize the eavesdropping risk and protect the communication between vehicle and server. The solution can be considered as adding the road side unit (RSU). In fact, RSU is an extra neural network between vehicle and server which does not generate any key; however, it transfers the key from vehicle to the server. In this model, cipher text (encryption) is generated by RSU. In addition, the filters in the convolutional layers are used for encrypting the messages and to protect it against eavesdropping efficiently. We formally verify the security functionality of the solution scheme by training the neural networks based on different parameters. Analysis shows that our proposal has a strong ability to prevent eavesdropping attack.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".