Two-Factor Localized Authentication Scheme for WLAN Roaming
Why this work is in the frame
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Bibliographic record
Abstract
In the paper, we propose an efficient two-factor localized authentication scheme suitable for WLAN roaming. The proposed authentication scheme can greatly improve the security compared with the previously reported counterparts, where two independent factors, such as "what you know" and "what you have", are utilized in the authentication process for a mobile user (MO). Some important issues specific to the wireless environment are considered in the design of the scheme, such as limited computation power, memory space, and battery capacity of mobile stations (MSs), and ping-pong movement problem when roaming across WLANs. The detailed implementation of the proposed scheme is presented, where some of the key performance measures and security are analyzed. Numerical results demonstrate that the proposed scheme can significantly outperform the legacy authentication schemes in terms of signaling overhead, power consumption, and authentication latency without losing the capability of preserving the system security.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it