COSPAS Search and Rescue Satellite Uplink: A MAC-Based Security Enhancement
Bibliographic record
Abstract
COSPAS-Sarsat is a global satellite-based search and rescue system that provides distress alert and location information to aid in the rescue of people in distress.However, recent studies show that the system lacks proper security mechanisms, making it vulnerable to various cyberattacks, including beacon spoofing, hacking, frequency jamming, and more.This paper proposes a backward-compatible solution to address these longstanding security concerns by incorporating a message authentication code (MAC) and timestamp.The MAC and timestamp ensure the integrity and authenticity of distress signals, while backward compatibility enables seamless integration with existing beacons.The proposed solution was evaluated in both a laboratory setting and a real-world satellite environment, demonstrating its practicality and effectiveness.Experimental results indicate that our solution can effectively prevent attacks such as spoofing, man-in-the-middle, and replay attacks.This solution represents a significant step toward enhancing the security of COSPAS-Sarsat beacon communication, making it more resilient to cyberattacks, and ensuring the timely and accurate delivery of distress signals to search and rescue authorities.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".