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Record W4290988721 · doi:10.5753/sbrc.2022.222339

Um Mecanismo de Proteção Ciente de Vias Aéreas Contra Jamming Attacks para a Internet dos Drones

2022· article· pt· W4290988721 on OpenAlexaff
Alisson R. Svaigen, Azzedine Boukerche, Linnyer B. Ruiz, Antônio A. F. Loureiro

Bibliographic record

Venuenot available
Typearticle
Languagept
FieldEngineering
TopicUAV Applications and Optimization
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsDroneHumanitiesJammingComputer sciencePolitical sciencePhysicsArtBiology

Abstract

fetched live from OpenAlex

A Internet dos Drones (IoD) é um paradigma de rede no qual os drones voam por vias aéreas bem definidas. O Jamming Attack (JA) representa um risco grave para a IoD, podendo afetar a trajetória dos drones. Embora o JA tenha sido investigado em redes de UAVs, as soluções existentes consideram o espaço aéreo livre para voo, enquanto na IoD os drones voam num espaço aéreo restrito. Para superar esse desafio, este trabalho apresenta o projeto de um mecanismo de proteção de vias aéreas contra JA em IoD. Nossos experimentos mostraram que nossa solução propõe um mecanismo de proteção adequado para evitar que drones sofram um JA, diminuindo o número de drones afetados. Assim, este trabalho avança os mecanismos de proteção para IoD.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.899
Threshold uncertainty score1.000

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.014
GPT teacher head0.241
Teacher spread0.227 · 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.

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
Published2022
Admission routes1
Has abstractyes

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