MétaCan
Menu
Back to cohort
Record W2066736649 · doi:10.1142/s0218194006002823

MODELING SECURE SYSTEMS USING AN AGENT-ORIENTED APPROACH AND SECURITY PATTERNS

2006· article· en· W2066736649 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 Software Engineering and Knowledge Engineering · 2006
Typearticle
Languageen
FieldComputer Science
TopicMulti-Agent Systems and Negotiation
Canadian institutionsCarleton University
Fundersnot available
KeywordsComputer scienceComputer security modelViewpointsSecurity engineeringSecurity testingSecurity information and event managementSoftware security assuranceSoftware engineeringInformation securitySecurity serviceComputer securityCloud computing security

Abstract

fetched live from OpenAlex

In this paper we describe an approach for modeling security issues in information systems. It is based on an agent-oriented approach, and extends it with the use of security patterns. Agent-oriented software engineering provides advantages when modeling security issues, since agents are often a natural way of conceptualizing an information system, in particular at the requirements stage, when the viewpoints of multiple stakeholders need to be considered. Our approach uses the Tropos methodology for modeling a system as a set of agents and their social dependencies, with specific extensions for representing security constraints. As an extension to the existing methodology we propose the use of security patterns. These patterns capture proven solutions to common security issues, and support the systematic and structured mapping of these constraints to an architectural model of the system, in particular for non-security specialists.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.547
Threshold uncertainty score0.794

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 head0.014
GPT teacher head0.231
Teacher spread0.217 · 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