Security Framework of Cloud Data Storage Based on Multi Agent System Architecture: Semantic Literature Review
Bibliographic record
Abstract
The purpose of this literature review is to provide the information about illustrating the usage of Multi-Agent System (MAS) techniques that can be beneficial in cloud computing platform to facilitate security of cloud data storage (CDS) among it. MAS are often distributed and agents have proactive and reactive features which are very useful for cloud data storage security (CDSS). The architecture of the system is formed from a set of agent’s communities. This paper of literature review described on the theoretical concept and approach of a security framework as well as a MAS architecture that could be implemented in cloud platform in order to facilitate security of CDS, on how the MAS technology could be utilized in a cloud platform for serving the security that is developed by using a collaborative environment of Java Agent DEvelopment (JADE). In order to facilitate the huge amount of security, our MAS architecture offered eleven security attributes generated from four main security policies of correctness, integrity, confidentially and availability of users’ data in the cloud. This paper of literature review also describes an approach that allows us to build a security cloud platform using MAS architecture and this architecture tends to use specialized autonomous agents for specific security services and allows agents to interact to facilitate security of CDS.
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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".