Improving the Security of Wireless Communications on High-Speed Trains by Efficient Authentication in SCN-R
Bibliographic record
Abstract
Recently, we have witnessed the remarkable development in high-speed railways around the world. To provide a robust and fast wireless network for the onboard passengers, we have earlier proposed smart collaborative networking for railways (SCN-R). In the realization of SCN-R, its security is challenged by potential exploitation of authentication vulnerabilities since traditional authentication mechanisms are unsuitable for scenarios with fast moving objects due to their complex and relatively timely operations. In this paper, we address this issue by proposing a new efficient authentication mechanism, which is based on a new design of chaotic random number generator (RNG). Comparing with the recent proposal relying on the precise boundaries of chaotic map state spaces, our RNG uses two logistic maps to avoid the time-consuming boundary location process. The proposed authentication mechanism uses the RNG to generate and validate the one-time password (OTP). To support different authentication applications, different lengths of OTPs can be used to differentiate and identify the applications. We have implemented our proposed authentication mechanism under real-world conditions, with results showing the feasibility and effectiveness of our authentication mechanism.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".