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Record W2620982535

IoT Security Adoption into Business Processes: A Socio-Technical View.

2017· article· en· W2620982535 on OpenAlexaff
Kavyashree Gollahalli Chandrashekhar, Forough Karimi-Alaghehband, Desiree Özgün

Bibliographic record

VenueJournal of the Association for Information Systems · 2017
Typearticle
Languageen
FieldComputer Science
TopicInformation and Cyber Security
Canadian institutionsHEC Montréal
Fundersnot available
KeywordsComputer scienceComputer securityBusiness
DOInot available

Abstract

fetched live from OpenAlex

Recently, the Internet of Things (IoT) has gained huge focus and has led to the generation of valuable data to create new value propositions for organisations. It is important to explore the impact these developments have on our society. IoT security is identified as the key issue amongst all the IoT applications and presents numerous social and technical challenges. We conducted interviews with IoT experts and the results illustrated how holistic security issues in IoT are undermined and to further emphasize the importance of addressing these issues by accommodating security into IoT business processes. This approach facilitated the assessment and identification of security threats from both social and technical perspectives. Our outcome highlights that IoT security must be implemented into IoT aware business processes to make the technology human centered, despite the challenges involved.

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 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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.948
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0020.007
Open science0.0020.000
Research integrity0.0000.000
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 head0.012
GPT teacher head0.263
Teacher spread0.251 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2017
Admission routes1
Has abstractyes

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