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Record W3109134412 · doi:10.32628/cseit1833773

Case Study of Cloud Computing Security and Emerging Security Research Challenges

2020· article· en· W3109134412 on OpenAlex
Shvetkumar Patel, Apeksha Pavasiya, S. Gomathi

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Scientific Research in Computer Science Engineering and Information Technology · 2020
Typearticle
Languageen
FieldComputer Science
TopicCloud Data Security Solutions
Canadian institutionsConcordia University
Fundersnot available
KeywordsCloud computingFlexibility (engineering)Cloud computing securityComputer securityComputer sciencePerspective (graphical)Internet privacy

Abstract

fetched live from OpenAlex

In this technological era, cloud computing is bombarded with immense benefits that includes availability, flexibility, ubiquitary access, and cost effectiveness. Cloud Computing offers its services to different kinds of users with the help of the World Wide Web on the virtual platform regardless of devices. Hence, all the resources kept on the same-shared storage device, which will lead to a considerable rise in various cloud security concerns for both; user and their private information. Data privacy can be compromised with a broad usage of cloud as smaller companies to bigger ones are adapting this versatile system. This paper examines several recent security attacks and proposed solutions of secure cloud computing from the perspective of organizations. Threat action varieties, other attacks with date, information exposed and number of record breach is presented with IT attack at risk. Finally, we have presented research challenges that can be worth noticing.

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 imitation

Not 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.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.963
Threshold uncertainty score0.738

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.004
Science and technology studies0.0000.001
Scholarly communication0.0010.004
Open science0.0020.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.075
GPT teacher head0.365
Teacher spread0.290 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it