Security, Privacy, and Safety Aspects of Civilian Drones
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
The market for civilian unmanned aerial vehicles, also known as drones, is expanding rapidly as new applications are emerging to incorporate the use of civilian drones in our daily lives. On one hand, the convenience of offering certain services via drones is attractive. On the other hand, the mere operation of these airborne machines, which rely heavily on their cyber capabilities, poses great threats to people and property. Also, while the Federal Aviation Administration NextGen project aims to integrate civilian drones into the national airspace, the regulation is still a work-in-progress and does not cope with their threats. This article surveys the main security, privacy, and safety aspects associated with the use of civilian drones in the national airspace. In particular, we identify both the physical and cyber threats of such systems and discuss the security properties required by their critical operation environment. We also identify the research challenges and possible future directions in the fields of civilian drone security, safety, and privacy. Based on our investigation, we forecast that security will be a central enabling technology for the next generation of civilian unmanned aerial vehicles.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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