Bayesian networks for modeling failure dependency in access control models
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
Access controls are indispensable mechanisms for protecting access to resources of computing and communication systems. Currently, the design of access control models is centered on the access interaction between system subjects and objects. However, access authentication, control, auditing and administration services in today's systems do not enjoy full operational independence while interacting with systems assets. That is, in a way or another they interact across different platforms, programs, processes or users, leading to build certain dependency while in operation. The identification and evaluation of this dependency is crucial to meeting security goals of access control models. To tackle this issue, we introduce a modeling technique that captures probabilistically the interaction between system assets and controls into a graph theoretic paradigm. We use Bayesian Networks (BN) in particular to model and analyze this dependency. We briefly show the proposed abstraction, modeling formalism and associated notation, along with a demonstration example of various useful inferences and some suggested research directions.
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 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 | 0.001 | 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 | 0.001 | 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 it