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Algorithmization for calculating the security assessment of AIS operating systems of internal affairs bodies, developed on the basis of an analysis of security requirements GOST R ISO/IEC 15408 and possible threats

2023· article· en· W4388022263 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueHerald of Dagestan State Technical University Technical Sciences · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEconomic and Technological Systems Analysis
Canadian institutionsCascades (Canada)
Fundersnot available
KeywordsGOST (hash function)Computer scienceRussian federationCommon CriteriaInformation security management systemSoftwareComputer securitySecurity information and event managementCloud computing securityBusinessOperating systemCloud computing

Abstract

fetched live from OpenAlex

Objective . The article provides a generalized algorithmization of the processes necessary for developing software for assessing the security of operating systems of automated information systems of internal affairs bodies of the Russian Federation. Method . The research was carried out based on the method of analyzing possible threats to the security of operating systems, as well as the requirements of the GOST R ISO/IEC 15408 standard. Result . The result of the automated system for calculating the security indicator of the analyzed OS is one of the specified criteria for indicators of the degree of security of the OS. By comparing the obtained indicator, the corresponding result is output. Conclusion . The authors provide a generalized algorithmization of the processes necessary for developing software for assessing the security of the AIS OS of the Russian Federation ATS.

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.

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)0.003
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.629
Threshold uncertainty score0.564

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
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 head0.043
GPT teacher head0.291
Teacher spread0.248 · 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