Evaluation of MUSER, a holistic security requirements analysis framework
Bibliographic record
Abstract
Security has been a growing concern for large organizations, especially financial and governmental institutions, as security breaches in the systems they depend on have repeatedly resulted in billions of dollars in losses per year, and this cost is on the rise. A primary reason for these breaches is that the systems in question are socio-technical - a mix of people, processes, technology and infrastructure. However, such systems are designed in a piecemeal rather than a holistic fashion, leaving parts of the system vulnerable. To tackle this problem, a three-realm security requirements framework was proposed to holistically analyse security requirements in different conceptual realms, including social realm (business processes, social actors), a software realm (software applications that support the social realm) and an infrastructure realm (physical and technological infrastructure). In this paper we evaluate this security requirements analysis framework. The evaluation was performed by two graduate students using a large scale case study on a medical emergency response system.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".