Trust the Crowd: Wireless Witnessing to Detect Attacks on ADS-B-Based Air-Traffic Surveillance
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
Surveillance-Broadcast (ADS-B) has been widely adopted as the de facto standard for air-traffic surveillance. Aviation regulations require all aircraft to actively broadcast status reports containing identity, position, and movement information. However, the lack of security measures exposes ADS-B to cyberattacks by technically capable adversaries with the purpose of interfering with air safety. In this paper, we develop a non-invasive trust evaluation system to detect attacks on ADS-B-based air-traffic surveillance using real-world flight data as collected by an infrastructure of ground-based sensors. Taking advantage of the redundancy of geographically distributed sensors in a crowdsourcing manner, we implement verification tests to pursue security by wireless witnessing. At the core of our proposal is the combination of verification checks and Machine Learning (ML)-aided classification of reception patterns-such that user-collected data cross-validates the data provided by other users. Our system is non-invasive in the sense that it neither requires modifications on the deployed hardware nor the software protocols and only utilizes already available data. We demonstrate that our system can successfully detect GPS spoofing, ADS-B spoofing, and even Sybil attacks for airspaces observed by at least three benign sensors. We are further able to distinguish the type of attack, identify affected sensors, and tune our system to dynamically adapt to changing air-traffic conditions.
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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 | 0.000 | 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