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Record W2636839313 · doi:10.1109/ccece.2017.7946790

The importance of human dynamics in the future user authentication

2017· article· en· W2636839313 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

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicUser Authentication and Security Systems
Canadian institutionsWestern University
Fundersnot available
KeywordsComputer scienceMulti-factor authenticationAuthentication (law)Computer securityPasswordGeneric Bootstrapping ArchitectureChallenge-Handshake Authentication ProtocolAuthentication protocolInternet privacyWorld Wide Web

Abstract

fetched live from OpenAlex

Authentication is one of the essential mechanism of a typical security model. It identifies the user legitimacy accessing any service over the network. Authentication can usually be done by a simple single-factor authentication method such as a password. Unfortunately, it is inadequate to ensure security when accessing variant resources and services across the Internet. Therefore, for users to authenticate their identity, there is always a need to work on a different security method that could be used as an extra layer of security can be implemented. This paper presents a human dynamic-based user authentication, relying on the assumption that the actions and interactions of personal, interpersonal, and social factors of a user would be very hard to impersonate. Furthermore, the study includes experiments to help understand human dynamics in dealing with individuals, groups, and societies to be the essential aspects for the new generation user authentication.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.896
Threshold uncertainty score0.436

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 studies0.0000.000
Scholarly communication0.0000.000
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 head0.016
GPT teacher head0.287
Teacher spread0.271 · 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

Quick stats

Citations7
Published2017
Admission routes1
Has abstractyes

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