Enhancing the Integrity of Automatic Dependent Surveillance-Broadcast Systems Using Format-Preserving Encryption: An Embedded System Solution
Bibliographic record
Abstract
The increasing adoption of Automatic Dependent Surveillance-Broadcast (ADS-B) has contributed to more efficient air traffic management by allowing aircraft to autonomously broadcast their flight data to Air Traffic Control (ATC) [1]. Despite its benefits, ADS-B remains vulnerable to attacks that threaten data integrity, such as message injection and modification [2]. While various mitigation techniques exist, many either require significant modification to existing infrastructure or major changes to the current ADS-B protocol, failing to protect the integrity of ADS-B in a practical way. This paper introduces a novel solution for enhancing ADS-B integrity using Format-Preserving Encryption (FPE) implemented on a low-cost embedded system. By installing an in-line device between the aircraft's ADS-B equipment and the ADS-B antenna, ADS-B messages are encrypted before being broadcast using an FPE algorithm, which preserves the format and length of ADS-B messages. Three FPE algorithms were implemented as part of this research: FF3, FFX, and AES-CTR. The embedded system and the three FPE algorithms were also evaluated for encryption and decryption times, Central Processing Unit (CPU) and memory usage, as well as thermal performance. The findings confirm that FPE can be practically applied to protect the integrity of ADS-B communications with minimal disruption to the current infrastructure.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 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 source (direct Gemma or distilled Codex), 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".