Analyzing security requirements as relationships among strategic actors
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.
Bibliographic record
Abstract
Abstract. Security issues for software systems ultimately concern relationships among social actors – stakeholders, users, potential attackers, etc.-- and software acting on their behalf. In assessing vulnerabilities and mitigation measures, actors make strategic decisions to achieve desired levels of security while trading off competing requirements such as costs, performance, usability and so on. This paper explores the explicit modeling of relationships among strategic actors in order to elicit, identify and analyze security requirements. In particular, actor dependency analysis helps in the identification of attackers and their potential threats, while actor goal analysis helps to elicit the dynamic decision making process of system players for security issues. Patterns of relationships at various levels of abstraction (e.g. intentional dependencies among abstract roles) can be studied separately. These patterns can be selectively applied and combined for analyzing specific system configurations. The approach is particularly suitable for new Internet applications where layers of software entities and human roles interact to create complex security challenges. Examples from Peer-to-Peer computing are used to illustrate the proposed framework. 1.
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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 | 0.000 | 0.000 |
| 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 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 it