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An Analysis of CVSS v2 Environmental Scoring

2011· article· en· W2544312844 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

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicInformation and Cyber Security
Canadian institutionsConcordia University of Edmonton
Fundersnot available
KeywordsComputer scienceCalculatorBase (topology)Range (aeronautics)Machine learningData miningArtificial intelligenceEngineeringMathematics

Abstract

fetched live from OpenAlex

This paper analyses the effect of the environmental metrics on the CVSS v2, and it shows that the environmental metrics impact the CVSS base score values in more ways than can be gleaned from the CVSS calculator provided by the NVD. This paper also unveils unexpected anomalies of "negative" calculated results of the Overall CVSS score when the base score is subjected to the environmental metrics. It also reveals that base scores of equal values do not necessarily remain equal when subjected to the environmental metrics. The presented results are based on a theoretical analysis of tthe formulas used in the CVSS v2 calculations. An approach to calculating the Overall CVSS score that eliminates the occurrence of "negative" values, and keeps the values within the range (0.0 -- 10.0) as defined in the guide for scoring vulnerabilities in the CVSS v2 is also suggested in this paper.

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)0.000
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.764
Threshold uncertainty score0.470

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 communication0.0000.001
Open science0.0000.000
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 head0.017
GPT teacher head0.213
Teacher spread0.197 · 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

Quick stats

Citations19
Published2011
Admission routes1
Has abstractyes

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