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Record W2403970759

A Survey on Requirements and Design Methods for Secure Software Development

2009· article· en· W2403970759 on OpenAlex
Muhammad Umair, Ahmed Khan, Mohammad Zulkernine

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
TopicAdvanced Malware Detection Techniques
Canadian institutionsQueen's University
Fundersnot available
KeywordsComputer scienceExecutableFinite-state machineSoftwareProgramming languageState (computer science)Context (archaeology)Abstract state machinesExtension (predicate logic)Computer securitySoftware engineering
DOInot available

Abstract

fetched live from OpenAlex

State Machine Language (AsmL) AsmL is an extended finite state machine-based executable software specification language which has also been used to specify attack scenarios [41]. The authors argue that due to the extended finite state machine-based nature of AsmL, attacks with multiple steps can be specified in AsmL. Such attack scenarios can be automatically translated into Snort rules which can then be used with an extension of the IDS Snort [41]. Such attack scenarios are able to capture more attacks with multiple steps using context information. Snort rules, the standard input for Snort, cannot represent attacks with multiple steps.

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: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.996
Threshold uncertainty score0.401

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.104
GPT teacher head0.399
Teacher spread0.295 · 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

Citations15
Published2009
Admission routes1
Has abstractyes

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