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Record W30663798 · doi:10.1002/ajh.25413

A Context-Aware Authentication Approach Based on Behavioral Definitions.

2010· article· en· W30663798 on OpenAlexaff
Cristiano Cortez da Rocha, João Carlos Damasceno Lima, Matheus Viera, Miriam A. M. Capretz, Michael Bauer, Iara Augustin, Mário A. R. Dantas

Bibliographic record

VenueIKE · 2010
Typearticle
Languageen
FieldComputer Science
TopicContext-Aware Activity Recognition Systems
Canadian institutionsWestern University
Fundersnot available
KeywordsComputer scienceAuthentication (law)Ubiquitous computingContext (archaeology)Mobile deviceComputer securityContext awarenessMobile computingHuman–computer interactionDistributed computingWorld Wide WebComputer network

Abstract

fetched live from OpenAlex

Mobile devices are becoming mandatory equipment in modern life and are increasingly being used as devices in various distributed computing environments to allow users to be connected to their companies and institutions anytime and anywhere. However, connections are usually based on traditional authentication processes, which do not consider the environmental characteristics, application’s requirements and information provided by sensors present in the pervasive space. A context-based approach can represent a useful alternative for circumventing threats in a mobile computing scenario. In this paper, it is presented an approach that adopts user authentication on mobile devices based on a spatio-temporal context. This behavior is modeled trough explicit and implicit profiles, which define events and tasks that compound the user activity. This approach presents a more dynamic and reliable policy for authenticating users. In addition, experimental results indicate a relevant efficiency of proposal using a space-time permutation model to detect authentication anomalies.

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 score0.850
Threshold uncertainty score0.699

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.066
GPT teacher head0.278
Teacher spread0.212 · 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
Published2010
Admission routes1
Has abstractyes

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