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Record W4200376676 · doi:10.18280/ijsse.110604

IoT Modes of Operations with Different Security Key Management Techniques: A Survey

2021· article· en· W4200376676 on OpenAlexvenueno aff
Samah Y. Mohamed, Ayman M. Hassan, Heba K. Aslan

Bibliographic record

VenueInternational Journal of Safety and Security Engineering · 2021
Typearticle
Languageen
FieldComputer Science
TopicIoT and Edge/Fog Computing
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceKey (lock)Table (database)Computer securityInternet of ThingsKey managementGateway (web page)Field (mathematics)Security associationSecurity serviceDefault gatewayMode (computer interface)Computer networkSecurity information and event managementCloud computing securityInformation securityCloud computingCryptographyWorld Wide WebDatabase

Abstract

fetched live from OpenAlex

The internet of things (IoT) has provided a promising opportunity to build powerful systems and applications. Security is the main concern in IoT applications due to the privacy of exchanged data using limited resources of IoT devices (sensors/actuators). In this paper, we present a classification of IoT modes of operation based on the distribution of IoT devices, connectivity to the internet, and the typical field of application. It has been found that the majority of IoT services can be classified into one of four IoT modes: gateway, device to device, collaborative, and centralized. The management of either public or symmetric keys is essential for providing security. In the present paper, we survey different key management protocols concerning IoT, which we further allocate in a map table. The map table is a link between modes of operation and the associated security key management elements. The main target of this mapping table is to help designers select the optimum security technique that provides the best balance between the required security level and IoT system mode constraints.

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)0.000
metaresearch head score (Gemma)0.000
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: none
Teacher disagreement score0.618
Threshold uncertainty score0.354

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.008
GPT teacher head0.221
Teacher spread0.214 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations4
Published2021
Admission routes1
Has abstractyes

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