Practical security for underwater acoustic networks: published results from the SAFE-UComm project
Bibliographic record
Abstract
The emergence of commercial underwater acoustic modems from different manufacturers and the promulgation of interoperability standards (e.g., JANUS) broadens the application scenarios of underwater acoustic telemetry and communications.At the same time, security concerns call for authentication and privacy-enforcing schemes.However, compute-or communication-intensive methods for terrestrial networks do not adapt well to bandwidthconstrained acoustic communications.In this context, we discuss the findings of the NATO SPS SAFE-UComm project, which involves research teams from Italy, Israel, Canada, and the UK.The project investigates and realizes practical security schemes that exploit the randomness of physical acoustic communication processes for security, and evaluates the potential of biomimicry and the capability of biomimetic signal detectors.After discussing the concept of SAFE-UComm, we survey its approaches to security through a number of results related to authentication, privacy, and biomimicry functions.Our results, based on several field experiments, show the feasibility of the project's design in relevant scenarios.
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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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".