Security Compliance Auditing of Identity and Access Management in the Cloud: Application to OpenStack
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
Cloud computing has seen a lot of interests and adoption lately. Nonetheless, the widespread adoption of cloud is still being hindered by the lack of transparency and accountability, which has traditionally been ensured through security compliance auditing techniques. Auditing in cloud, however, presents many new challenges in data collection and processing (e.g., data format inconsistency and lack of correlation due to the heterogeneity of cloud infrastructures) and in verification (e.g., prohibitive performance overhead due to the sheer scale of cloud infrastructures and their self-provisioning, elastic, and dynamic nature). In this paper, we propose a security compliance auditing framework for cloud, with special focus on identity and access management, and we implement and evaluate the framework based on OpenStack, one of the most popular cloud management systems. Our experimental results show that auditing with formal methods in large cloud environment is realistic (e.g., our auditing solution can handle 60 thousand users in less than one minute).
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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