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Record W2077108401 · doi:10.5539/ass.v6n12p50

Honeypot Protection Detection Response Recovery Model for Information Security Management Policy

2010· article· en· W2077108401 on OpenAlexvenueno aff
Shu Bao Xing, Hui Xue, Li Gang

Bibliographic record

VenueAsian Social Science · 2010
Typearticle
Languageen
FieldComputer Science
TopicInformation and Cyber Security
Canadian institutionsnot available
Fundersnot available
KeywordsHoneypotInformation securityComputer securityInformation security managementComputer scienceSecurity managementProcess (computing)Incident managementBusinessStandard of Good PracticeSecurity information and event managementFunction (biology)Security policyScheme (mathematics)Process managementKnowledge managementNetwork security policyCloud computing securitySecurity service

Abstract

fetched live from OpenAlex

Focusing on characteristics of information management in SME that most information security models assign the detection function to the operational process of enterprise, this paper proposes an information security management model for SMEs ---- the HPDRR (Honeypot Protection Detection Response Recovery) model. By means of empirical studies, this paper summarizes the scheme for executing, maintaining, and improving the information security management policy in SMEs in China.

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)0.002
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 score0.981
Threshold uncertainty score0.900

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.006
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 head0.009
GPT teacher head0.252
Teacher spread0.243 · 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

Citations2
Published2010
Admission routes1
Has abstractyes

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