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Record W2132454948 · doi:10.1504/ijsn.2011.043668

Improved IP Multimedia Subsystem authentication mechanism for 3G-WLAN networks

2011· article· en· W2132454948 on OpenAlexaff
Madhu Sharma, Victor C. M. Leung

Bibliographic record

VenueInternational Journal of Security and Networks · 2011
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Authentication Protocols Security
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsComputer scienceComputer networkIP Multimedia SubsystemAuthentication protocolAuthentication (law)Overhead (engineering)Quality of serviceLightweight Extensible Authentication ProtocolChallenge-Handshake Authentication ProtocolComputer security

Abstract

fetched live from OpenAlex

The provision of IP Multimedia Subsystem (IMS) introduces important advantages for users of 3G-WLAN networks. However, a multi-pass authentication procedure needs to be performed before accessing the IMS, resulting in added overhead and possible Quality of Service (QoS) degradations. The problem is further compounded when the user moves from one Wireless Local Area Network (WLAN) domain into another, which requires the authentication procedure to be repeated. To mitigate this problem, we present a lightweight, robust, and architecture-compatible IMS authentication protocol that implements a one-pass IMS procedure by promoting efficient key re-use for a mobile user. The security of the proposed protocols is verified using the Automated Validation of Internet Security Protocols and Applications (AVISPA) security analyser. We also analyse the authentication delay of our proposed scheme. Numerical results reveal a reduction in user authentication delay of more than 50% compared to the existing authentication procedure.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.020
GPT teacher head0.267
Teacher spread0.247 · 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 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

Citations17
Published2011
Admission routes1
Has abstractyes

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