Group Authentication for Drone Swarms
Why this work is in the frame
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Bibliographic record
Abstract
In parallel with the advances of aerial networks, the use of drones is quickly included in daily activities. According to the characteristics of the operations to be carried out using the drones, the need for simultaneous use of one or more drones has arisen. The use of a drone swarm is preferred rather than the use of a single drone to complete activities such as secure crowd monitoring systems, cargo delivery. Due to the limited airtime of the drones, new members may be included in the swarm, or there may be a unification of two or more drone swarms when needed. Authentication of the new drone that will take its place in the drone swarm and the rapid mutual-verification of two different swarms of drones are some of the security issues in the swarm structures. In this study, group authentication-based solutions have been put forward to solve the identified security issues. The proposed methods and 5G new radio (NR) authentication methods were compared in terms of time and a significant time difference was obtained. According to the 5G NR standard, it takes 22 ms for a user equipment (UE) to be verified by unified data management (UDM), while in the proposed method, this time varies according to the threshold value of the polynomial used and it is substantially lower than 22 ms for most threshold values.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.010 | 0.011 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.001 | 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