Using Expert Systems to Statically Detect "Dynamic" Conflicts in XACML
Bibliographic record
Abstract
Policy specification languages such as XACML often provide mechanisms to resolve dynamic conflicts that occur when trying to determine if a request should be permitted or denied access by a policy. Examples include "deny-overrides" or "first-applicable." Such algorithms are primitive and potentially a risk for corporate computer security. While they can be useful for resolving dynamic conflicts, they are not justified for conflicts that can be easily detected statically. It is better to find those at compile time and remove them before run time. Many different approaches have been used for static conflict detection. However, most of them do not scale well because they rely on pair-wise comparison of the access control logic of policies and rules. We propose an extension of a Prolog-based expert system approach due to Eronen and Zitting. This approach uses constraint logic programming techniques (CLP), which are well-adapted to hierarchical XACML policy logic and avoid pair-wise comparisons altogether by taking advantage of Prolog's built-in powerful indexing system. We demonstrate that expert systems can indeed detect conflicts statically, even those that are generally believed to only be detectable at run time, by inferring the values of attributes that would cause a conflict. As a result, relying on the XACML policy combining algorithms can be avoided in most cases except in federated systems. Finally we provide performance measurements for two different architectures represented in Prolog and give some analysis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), 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".