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A Survey of Authentication Protocols for Enhancing Security in Underwater Communication Systems

2025· article· en· W4412568078 on OpenAlexaff
Sai Varshitha. G, Ch. Rupa, D Divya, Thippa Reddy Gadekallu, Gautam Srivastava

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicUnderwater Vehicles and Communication Systems
Canadian institutionsBrandon University
Fundersnot available
KeywordsComputer scienceComputer securityAuthentication (law)UnderwaterAuthentication protocolCryptographic protocolUnderwater acoustic communicationComputer networkCryptographyGeologyOceanography

Abstract

fetched live from OpenAlex

The growing importance of underwater communication systems spans various domains, including military operations, environmental monitoring, and resource exploration. However, the unique characteristics of the underwater environment, such as signal attenuation, limited bandwidth, and fluctuating channel conditions, pose significant challenges to ensuring reliable and secure communication. This study presents an in-depth exploration of authentication protocols aimed at securing underwater communication systems, focusing on key aspects of system performance. The proposed framework incorporates advanced communication methods, including acoustic, optical, and electromagnetic techniques, to enhance data transmission efficiency. Additionally, a comparative analysis of recent research highlights the progress, strengths, and limitations of current underwater communication technologies, emphasizing developments in deep learning applications, adaptive protocols, and energy-efficient solutions. The study also addresses critical challenges like energy limitations, environmental variability, and the resilience of protocols in dynamic underwater environments. Overcoming these challenges is crucial for improving the reliability, scalability, and security of communication systems in complex underwater conditions.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.727
Threshold uncertainty score0.301

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.034
GPT teacher head0.301
Teacher spread0.267 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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