Threat Analysis of a Long Range Autonomous Unmanned Aerial System
Bibliographic record
Abstract
Technology of the 21st century has led to the development and deployment of many unmanned aerial vehicles (UAVs) within today's airspace. UAVs typically perform tasks such as surveillance, pipeline, and crop monitoring, professional imaging, surveying, search and rescue, and military operations. Many of these UAVs execute their duties entirely autonomously without the intervention of humans. Due to the nature of the responsibilities of UAVs, their security is of utmost importance. Security threats to UAVs are often targeted at the unmanned aerial system (UAS) which includes everything employed to allow the UAV to function; this can include the software running on the drone, the control system piloting the drone and the connection between the two. This paper provides an overview of autonomous UAS architecture and analyzes security threats to the system. The goal of this paper is to support UAV manufacturers and developers to have an understanding of the components required in an autonomous UAS and allow them to identify, prevent and address security concerns within their systems.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".