A Framework-Driven Mapping of ADS-B Vulnerabilities to NIST RMF and AI RMF
Bibliographic record
Abstract
The increase in worldwide flight traffic and the growing number of aerial vehicles necessitate the incorporation of innovative technologies into aviation regulatory practices. The Automatic Dependent Surveillance-Broadcast (ADS-B) system complies with the International Civil Aviation Organization’s recent flight safety regulations and is now widely adopted by most airlines. Nevertheless, ADS-B features some serious vulnerabilities due its open nature which may results in several attacks such as spoofing endangering flight operations. In addition, ADS-B governance requirements vary significantly per regions while AI-based methods lack formal oversight and alignment with AI governance. In this paper, we bridge this gap by providing a mapping of ADS-B vulnerabilities, attacks and AI-based solutions with the National Institute of Standards and Technology (NIST) Risk Management Framework (RMF), and the NIST AI RMF. We also highlight the ADS-B mandates in case studies focusing on USA, Europe, Canada, China and Australia. Our findings highlights the gap in applying cybersecurity and AI risk management frameworks in aviation. This paper would serve as a reference for key stakeholders in aviation aiming to conduct further research in this field and establish a foundational layered governance framework that addresses all the highlighted gaps in this work.
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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 | 0.000 | 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) | 0.001 | 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 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".