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Record W2548482289 · doi:10.1109/ccece.2016.7726674

An architecture for a secured tunnel in the Automatic Multicast Tunneling (AMT) environment

2016· article· en· W2548482289 on OpenAlexafffund
Abonti Ferdous, J. William Atwood

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicIPv6, Mobility, Handover, Networks, Security
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMulticastComputer networkXcastSource-specific multicastProtocol Independent MulticastComputer sciencePragmatic General MulticastIP multicastInter-domainMulticast addressUnicastDistributed computing

Abstract

fetched live from OpenAlex

IP multicasting is a communication mechanism in which identical data are communicated from a server to multiple clients. It has two main problems: there is no control over who can receive the multicast data, and every receiver has to be in a network where multicast routing is enabled. Islam and Atwood have shown how to add Receiver Access Control (RAC) to IP multicast. Automatic Multicast Tunneling (AMT) offers a mechanism to enable the clients in a unicast-only region to receive multicast data through an automatically-established tunnel. However, AMT, like the IP multicast that it extends, does not provide any access control over the receivers or any security features. Malla and Atwood have demonstrated how to integrate RAC into the AMT environment, assuming that the AMT tunnel is secure. In this paper, we show how to establish the necessary verifiable identities for the tunnel end points, so that the tunnel can be secured. We also validate the security of our design using the Automated Validation of Internet Security Protocols and Applications (AVISPA) tool.

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)0.001
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.489
Threshold uncertainty score0.568

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 science0.0000.000
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 head0.009
GPT teacher head0.213
Teacher spread0.205 · 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

Citations1
Published2016
Admission routes2
Has abstractyes

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