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Record W2007232223 · doi:10.1109/sere.2013.11

A Security Risk Assessment Framework for SysML Activity Diagrams

2013· article· en· W2007232223 on OpenAlex
Samir Ouchani, Otmane Aı̈t Mohamed, Mourad Debbabi

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
Fundersnot available
KeywordsComputer scienceSystems Modeling LanguageActivity diagramProbabilistic logicComputer security modelUnified Modeling LanguageSoftware engineeringComputer securityProgramming languageSoftwareArtificial intelligence

Abstract

fetched live from OpenAlex

In this paper, we address the issue of security risk assessment of systems that are designed by using SysML activity diagrams. For this purpose, we develop a practical framework to enable security requirements specification and security level evaluation. First, we rely on the standard catalogue of attacks to build a library of attacks patterns. Then, we model the extracted patterns as SysML activity diagrams and we develop a specification algorithm in order to automatically generate security requirements relevant to a system under test. In order to evaluate them, we propose a methodology to map the diagrams interaction into a probabilistic model checker. Finally, we demonstrate the effectiveness of our framework on the secure real time streaming protocol.

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: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.797
Threshold uncertainty score0.458

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 communication0.0000.002
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 head0.010
GPT teacher head0.275
Teacher spread0.264 · 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

Citations22
Published2013
Admission routes1
Has abstractyes

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