Physical Layer Node Authentication in Underwater Acoustic Sensor Networks Using Time-Reversal
Bibliographic record
Abstract
Underwater acoustic sensor networks (UASNs) have played a vital role in many security-sensitive applications like offshore oil exploration, tsunami forecast, and tactical surveillance. Due to the complex marine environment and harsh acoustic channel, UASNs face severe security challenges and attacks. Exploiting the multi-path energy from the richly scattering underwater environment, the time-reversal (TR) process can form the resonating strength based on the channel impulse response (CIR). Inspired by the natural link signature resulting from the spatial dependency of acoustic links, an authentication scheme using the maximum TR resonating strength is proposed, with the aim of effectively detecting the spoofing attacks in UASNs. To accommodate the time-varying nature of the underwater acoustic link, a database correlation method is exploited to capture the link CIR pattern over time for each link, which efficiently improves the accuracy of the authentication scheme using the TR process. Hence, the proposed algorithm enables each node to make the authentication decision based on maximum time-reversal resonating strength (MTRRS) locally with little overhead. It was evaluated by the probabilities of authentication, attack detection, and false alarm through extensive simulations. Finally, this MTRRS-based authentication was verified using the sea trial CIR data.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".