Security Features for Proximity Verification
Bibliographic record
Abstract
Proximity identification has been widely used on various applications. These applications provide users with convenience and efficiency, however, they are vulnerable to various attacks, such as skimming, eavesdropping, and relay attacks. Modern mobile devices are equipped with sophisticated sensors and receivers to facilitate the process of proximity verification by collecting and comparing information retrieved from the environment. In this paper, we propose multiple physical security features which are accessible through smart devices, namely, RSSI (Receiving Signal Strength Indicator), round-trip time, GPS (Global Positioning System) coordinates, and Wi-Fi access point lists, to precisely identify the close proximity of two devices. These security features are highly efficient to provide proof of physical proximity. We first evaluate each physical security feature individually, through real-life experiments, to demonstrate their efficacy in identifying environment characteristics. Then, we evaluate the performance of each security feature using Wilcoxon test. The results show that the proposed security features are effective in identifying physical proximity and can be combined for better accuracy.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".