Securing D2D Wireless Links by Continuous Authenticity with Legitimacy Patterns
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Device-to-Device (D2D) communications enables the efficient use of wireless system resources (i.e., power and spectrum) and promotes users' privacy but may increase the likelihood of cyber-attacks due to the lack of security infrastructure. This paper utilizes continuous authenticity to develop a security-scoring measure that can evaluate and help improve the security of current D2D wireless systems, and improve the design of 5G future systems, such as LTE-Direct. Simulation results are presented to show the feasibility of implementing the proposed security-scoring using legitimacy patterns, and to compare security-scoring results from static and random allocation of legitimacy patterns. Future legitimacy patterns could account for both technical considerations and human behavior to achieve higher performance.
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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 | 0.000 |
| Open science | 0.000 | 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