ADS-B Authentication Compliant with Mode-S Extended Squitter Using PSK Modulation
Bibliographic record
Abstract
This work proposes to use Differential 16 Phase-Shift Keying (D16PSK) modulation for embedding a digital signature within Mode-S Extended Squitter (ES) Automatic Dependent Surveillance-Broadcast (ADS-B) messages. The purpose is to provide a security mechanism in order to protect systems against the injection of malicious Hazardously Misleading Information (HMI). The use of digital signatures for ADS-B has been previously analyzed in the literature. Our contribution and focus are devoted to the physical layer used to transmit such a signature. The proposed method generates ADS-B messages compliant with current standards, thus can be interpreted by current equipment. However, only a modified receiver shall be able to extract the embedded signature and authenticate the message. This work describes possible architectures for the ADS-B transmitting and receiving devices. An analysis of the expected maximum range and bit error rate for various PSK modulations is provided. In addition, the use of timestamps to protect against message replays is proposed and studied in the final part of the paper. In summary, this paper proposes a backward compatible way to secure the ADS-B system for the Mode-S ES data link, providing regulatory authorities with the means in case they opt for the use of digital signatures. The proposal is inspired from the idea behind the wireless security encryption method of Nagravision used in the Satellite TV broadcasting.
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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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".