A security-as-a-service solution for applications in cloud computing environment
Bibliographic record
Abstract
Cloud computing is achieving tremendous popularity these days, by sharing software, platform and infrastructure through virtualization across various organizations and individuals, it offers easy access to computing power that greatly exceeds what one can access in his old physical world. Although almost all cloud vendors claim that their clouds are safe, security concerns are still raised widely among cloud users. The concerns not only come from users who hold sensitive user data, but also from the security responsibilities that cloud users need to take for their applications and the cloud environment they own. However, the cloud vendors have not yet provided an unified security service that could cover all stages in the software development life cycle (SDLC), while conventional security protection mechanisms that may be effective and efficient for on-premise architecture may not be suitable for the new cloud architecture. Especially, for small businesses who wish to leverage cloud computing but usually do not have access to a complete list of security services throughout the entire SDLC, it becomes greatly desirable to design an integrated service that aims to meet the security needs from various aspects. This papers explores in-depth what specific security requirements / concerns that cloud environment has, and more importantly, a security-as-a-service (SECaaS) solution is proposed to provide an end-to-end solution for organizations or individuals who need to deploy their applications onto cloud.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".