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Record W2118858431 · doi:10.1155/2009/716480

Secure Media Independent Handover Message Transport in Heterogeneous Networks

2009· article· en· W2118858431 on OpenAlexaff
Jeong-Jae Won, Murahari Vadapalli, Choong-Ho Cho, Victor C. M. Leung

Bibliographic record

VenueEURASIP Journal on Wireless Communications and Networking · 2009
Typearticle
Languageen
FieldEngineering
TopicIPv6, Mobility, Handover, Networks, Security
Canadian institutionsUniversity of British Columbia
FundersNational Research Foundation of KoreaNational Research Foundation
KeywordsComputer scienceComputer networkIPsecHandoverHeterogeneous networkAuthentication (law)Message authentication codeWireless networkWirelessComputer securityCryptographyThe InternetTelecommunications

Abstract

fetched live from OpenAlex

The IEEE 802.21 framework for Media Independent Handover (MIH) provides seamless vertical handover support for multimode mobile terminals. MIH messages are exchanged over various wireless media between mobile terminals and access networks to facilitate seamless handover. This calls for the need to secure MIH messages against network security threats in the wireless medium. In this paper, we first analyze IPSec/IKEv2 and DTLS security solution for secure MIH message transport. We show that handover latency can be an impediment to the use of IPSec and DTLS solutions. To overcome the handover overhead and hence minimize authentication time, a new secure MIH message transport solution, referred as MIHSec in this paper, is proposed. Experimental results are obtained for MIH between WLAN and Ethernet networks and the impacts of MIH message security on the handover latency are evaluated for IPSec, DTLS, and MIHSec security solutions. The effectiveness of MIHSec is demonstrated.

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)0.003
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: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.234
Teacher spread0.218 · 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

Citations8
Published2009
Admission routes1
Has abstractyes

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