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A Hybrid Model for Information Security Risk Assessment

2019· article· en· W2947032870 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

VenueInternational Journal of Advanced Trends in Computer Science and Engineering · 2019
Typearticle
Languageen
FieldComputer Science
TopicInformation and Cyber Security
Canadian institutionsAthabasca University
Fundersnot available
KeywordsRisk analysis (engineering)Risk managementRisk assessmentProcess (computing)Security managementComputer scienceInformation securityInformation security managementStandard of Good PracticePerspective (graphical)Process managementManagement scienceBusinessComputer securitySecurity information and event managementEngineeringCloud computing security

Abstract

fetched live from OpenAlex

Many industry standards and methodologies were introduced which has brought forth the management of threats assessment and risk management of information assets in a systematic manner. This paper will review and analyze the main processes followed in IT risk management frameworks from the perspective of the threat analysis process using a threat modeling methodology. In this study, the authors propose a new assessment model which shows that systematic threat analysis is an essential element to be considered as an integrated process within IT risk management frameworks. The new proposed model complements and fulfills the gap in the practice of assessing information security risks.

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)0.001
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: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.635
Threshold uncertainty score0.444

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.006
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 head0.006
GPT teacher head0.261
Teacher spread0.255 · 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