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Record W1618210274 · doi:10.5555/2048558.2048572

An overview of cyber attack and computer network operations simulation

2011· article· en· W1618210274 on OpenAlexaff
Sylvain Leblanc, Andrew Partington, Ian M. Chapman, Mélanie Bernier

Bibliographic record

VenueAnnual Simulation Symposium · 2011
Typearticle
Languageen
FieldDecision Sciences
TopicSimulation Techniques and Applications
Canadian institutionsDefence Research and Development CanadaRoyal Military College of Canada
Fundersnot available
KeywordsConstructiveSnapshot (computer storage)CyberwarfareNetwork-centric warfareComputer scienceModeling and simulationElectronic warfareComputer securityState (computer science)Simulation modelingGovernment (linguistics)SimulationTelecommunications

Abstract

fetched live from OpenAlex

This paper represents a snapshot of the current state of the art in the simulation and modeling of cyber attacks and defensive responses to those. It discusses a number of simulations of cyber warfare, including live, virtual, and constructive simulations. The simulations discussed in this paper were found in the open literature and were conducted in the private sector, academia, and government. Each simulation is briefly described, including goals, methodology, and a brief discussion of its accomplishments. These modeling and simulation efforts are of particular interest to the military modeling and simulation community, as it is likely that military forces will continue to rely ever more heavily on computer and communication networks.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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: Review · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.004
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.002

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 head0.235
GPT teacher head0.453
Teacher spread0.218 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

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

Citations22
Published2011
Admission routes1
Has abstractyes

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