Enhancing UAV Security through Comprehensive Vulnerability Analysis and an ARM PSA-Based Architecture
Bibliographic record
Abstract
Recently, Unmanned Aerial Vehicles (UAVs), which are embedded, real-time systems, are being used in various fields such as military, logistics, surveillance, and mapping.UAVs face security threats due to their diverse peripherals and attack vectors.Real-world incidents, such as GPS spoofing and control signal manipulation, highlight the need for improved security systems in UAVs to prevent potential human losses and material damage.While prior research has examined UAV security vulnerabilities, it has primarily focused on individual attack techniques rather than systematically analyzing the relationship between software and hardware security risks.As such, effectively addressing these security threats necessitates a comprehensive analysis of the software and hardware architecture of UAVs and a systematic investigation of existing attack cases.This paper systematically analyzes UAV attack cases and defines a generalized UAV architecture based on commercial and opensource UAV platforms.Furthermore, we evaluate the security vulnerabilities in widely used UAV software frameworks, including PX4 Autopilot and the MAVLink communication protocol.Based on our findings, we propose a Secure UAV architecture leveraging ARM Platform Security Architecture (PSA) to enhance resilience against cyber threats.
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.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".