Towards a secure hybrid adaptive gateway discovery mechanism for intelligent transportation systems
Bibliographic record
Abstract
Abstract In the recent years, we are witnessing a growing interest into the design of smart vehicles and smart roads for Intelligent transportation systems. Vehicles as part of the Internet of Things should provide to the driver and passenger with a variety of services using efficient gateway discovery mechanism while maintaining a certain level of security and authentication to avoid potential malicious attacks. In this paper, we propose a secure hybrid adaptive gateway discovery and communication protocol for smart vehicular networks, which we refer to as SEGAL. Our proposed SEGAL protocol is based upon building a secure clustered vehicular network, and permits the exchange of gateway discovery messages through authenticated clusterheads and cluster members. We shall present the design of our protocol, and describe how it can overcome the possible malicious attacks that might harm the network. Then, we report its efficiency and scalability using an extensive set of simulation experiments using Ns‐2 simulator. Our results indicate that the proposed SEGAL protocol is scalable while achieving high success rate, low response time and dropping rate. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".