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Gamifying a 5G Core to Create a Capture the Flag Cyber Security Training Environment

2024· article· en· W4392248493 on OpenAlexafffund
Peiqi Paige Wang, Amina Shafo, Longpeng Angus Xu, Zhichuan Zhao, Wahab Almuhtadi, Jordan Melzer, Wynn Fenwick

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Malware Detection Techniques
Canadian institutionsTelus (Canada)Algonquin College
FundersScience and Engineering Research CouncilMitacs
KeywordsFlag (linear algebra)Computer scienceCore (optical fiber)Computer securityTraining (meteorology)Internet privacyTelecommunicationsPhysics

Abstract

fetched live from OpenAlex

Fifth Generation cellular data systems (5G) are critical infrastructure with high security demands and complex architectures. Despite the importance of these systems, there is relatively little hands-on training available for 5G engineers and security practitioners. We propose to adapt a well-designed cyber security training game –Capture the Flag– to provide compelling, accessible hands-on 5G training. We present C5G (Capture the Flag 5G): a project developing 5G Capture the Flag by integrating open source 5G components into an open source Capture the Flag game engine. In C5G, players or teams race against each other and the clock to solve 5G hacking challenges on virtual 5G infrastructure.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.920
Threshold uncertainty score0.394

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 head0.030
GPT teacher head0.272
Teacher spread0.242 · 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 designNot applicable
Domainnot available
GenreMethods

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
Published2024
Admission routes2
Has abstractyes

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