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Record W4240459155 · doi:10.32920/ryerson.14652960.v1

Authentication protocols for smart homes

2021· preprint· en· W4240459155 on OpenAlexaff
Maninder Singh Raniyal

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldComputer Science
TopicAdvanced Authentication Protocols Security
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsPasswordMutual authenticationComputer scienceAuthentication (law)Multi-factor authenticationComputer securitySmart cardAuthentication protocolScheme (mathematics)Computer networkChallenge–response authenticationLightweight Extensible Authentication Protocol

Abstract

fetched live from OpenAlex

One of the IoT's greatest opportunity and application still lies ahead in the form of smart home. In this ubiquitous/automated environment, due to the most likely heterogeneity of objects, communication, topology, security protocols, and the computationally limited na- ture of IoT objects, conventional authentication schemes may not comply with IoT security requirements since they are considered impractical, weak, or outdated. This thesis proposes: (1) The design of a two-factor device-to-device (D2D) Mutual Authentication Scheme for Smart Homes using OTP over Infrared Channel (referred to as D2DA-OTP-IC scheme); (2) The design of two proxy-password protected OTP-based schemes for smart homes, namely, the Password Protected Inter-device OTP-based Authentication scheme over Infrared Chan- nel and the Password Protected Inter-device OTP-based Authentication scheme using public key infrastructure; and (3) The design of a RSA-based two-factor user Authentication scheme for Smart Home using Smart Card.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.009
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.006
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0090.003

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 head0.047
GPT teacher head0.359
Teacher spread0.313 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

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

Citations0
Published2021
Admission routes1
Has abstractyes

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