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Record W6912624080 · doi:10.5281/zenodo.5111966

Challenges Cybersecurity Architects Are Facing in a Cloud Computing Environment

2021· article· en· W6912624080 on OpenAlex

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

VenueZenodo (CERN European Organization for Nuclear Research) · 2021
Typearticle
Languageen
FieldComputer Science
TopicCloud Data Security Solutions
Canadian institutionsLockheed Martin (Canada)
Fundersnot available
KeywordsCloud computingProvisioningCloud computing securitySoftware as a serviceService providerUtility computingService (business)Cloud testing

Abstract

fetched live from OpenAlex

In the past decade, cloud computing has become an integral part of many companies’ business strategies and<br> IT architecture. Companies look to seek and adopt new business models, increase efficiency in handling massive amount of<br> data, handle fluctuations in computing workloads for customers and stakeholders, and gain a competitive advantage in<br> their industry. All these concepts have to be considered while also trying to deliver a product or service, and not disrupt<br> existing operations for the company. This paper will address the multilevel challenges and threats in cloud computing and<br> their potential solutions.<br> Cloud adoption has introduced the three types of cloud computing service models. The first is the Infrastructure as a<br> Service (IaaS) model, which is defined as an instant computing infrastructure that is provisioned and managed over the<br> internet. The second is the Platform as a Service (PaaS) model, in which companies essentially rent everything they need to<br> build an application and rely on the cloud provider for development tools, infrastructure, and operating systems. The third<br> is Software as a Service (SaaS) model, which is a software distribution model in which a cloud service provider will host<br> applications for customers and makes them available over the internet.<br> Many companies have developed a new approach called hybrid cloud computing. The growth of the hybrid cloud model<br> has allowed companies to use a mix of the three models with public and private clouds to create the best environment for<br> their company’s infrastructure. The top benefits of this approach include: Better security, Operating cost, improvements,<br> and Speed and agility increase.<br> A hybrid cloud model can eliminate or greatly reduce trade-offs and offer the best solutions for the company.<br> Implementation and management can still be challenging for a hybrid cloud model. Having different management tools for<br> a private or public cloud, introduces a fragmented IT infrastructure that strongly lacks interoperability and visibly for the<br> company.<br> Keywords-component; cybersecurity; cloud computing; hybrid cloud

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.922
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.004
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.002

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.

Opus teacher head0.052
GPT teacher head0.245
Teacher spread0.193 · 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