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Record W2909858130 · doi:10.1109/oceans.2018.8604561

Channel Access Model to Predict Impact of Authentication Attack on AIS

2018· article· en· W2909858130 on OpenAlexaff
Surender Singh, H. G. Rezaii, Jean‐François Bousquet, James C. Craig

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicNetwork Security and Intrusion Detection
Canadian institutionsDalhousie University
Fundersnot available
KeywordsComputer scienceComputer networkAuthentication (law)Computer securityChannel (broadcasting)Multi-factor authenticationAuthentication protocol

Abstract

fetched live from OpenAlex

Recommendation ITU-R M.1371-5 from ITU-R provides technical characteristics for an automatic identification system using time division multiple access in the VHF maritime mobile frequency band. Security is an important aspect in Automatic Identification System (AIS) for global maritime applications. For short range data exchange, AIS radios transmit in the VHF band, and Self-Organizing Time Division Multiple Access (SOTDMA) manages the channel resources that can be shared by multiple users. The challenge of the system mainly lies in the overall system security. In this work, the reliability of the network is evaluated as the traffic increases in a local area, representative of a seaport. The performance is assessed as a function of number of nodes and report rate. Also, the degradation of the network performance is evaluated as an attacker attempts to flood the network.

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)0.000
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.655
Threshold uncertainty score0.236

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0010.000
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 head0.067
GPT teacher head0.351
Teacher spread0.284 · 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

Citations4
Published2018
Admission routes1
Has abstractyes

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