SecSLA: A Proactive and Secure Service Level Agreement Framework for Cloud Services
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 customers migrate to cloud services to reduce the operational costs of information technology (IT) and increase organization efficiency. However, ensuring cloud security is very challenging. As a consequence, cloud service providers find it difficult to persuade customers to acquire their services due to security concerns. In terms of outsourcing applications, software, and/or infrastructure services to the cloud, customers are concerned about the availability, integrity, privacy, and legality of the hosted service. In this paper, a secure service level agreement (SecSLA) framework is proposed to alleviate these concerns and provide security control assurance to cloud customers. The framework is proactive in detecting violations of SecSLA parameters based on a cloud security operations center as a service (SOCaaS). In addition, a trusted third party can use this framework to audit and monitor SecSLA compliance.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 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