Bibliographic record
Abstract
We pose and study forensic analysis in the context of access control systems in a manner that prior work has not. Forensics seeks to answer questions about past states of a system, and thereby provides important clues and evidence in the event of a security incident. Access control deals with who may perform what action on a resource and is a critical security function. Our focus is access control systems that allow for changes to the authorization state to be delegated to potentially untrusted users. We argue that this context in access control is an important one in which to consider forensic analysis, and observe that it is a natural complement of safety analysis, which has been considered extensively in the literature. We pose the forensic analysis problem for such access control systems abstractly, and instantiate it for three schemes from the literature: a well-known access matrix scheme, a role-based scheme, and a discretionary scheme. We identify the computational complexity of forensic analysis, and compare it to that of safety analysis for each of the schemes. We consider also the notion of logs, i.e., data that can be collected over time to aid forensic analysis.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".