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Record W2106397303 · doi:10.1080/19393555.2010.493598

SSH-Based Device Identity and Trust Initialization

2010· article· en· W2106397303 on OpenAlexaff
Jin Peng, Xin Zhao

Bibliographic record

VenueInformation Security Journal A Global Perspective · 2010
Typearticle
Languageen
FieldEngineering
TopicIPv6, Mobility, Handover, Networks, Security
Canadian institutionsNortel (Canada)
Fundersnot available
KeywordsInitializationComputer scienceProtocol (science)Key (lock)Trust anchorIdentity (music)Computer securityIdentification (biology)Computer networkChannel (broadcasting)Computational trustPhysicsPolitical science

Abstract

fetched live from OpenAlex

Managing devices in distributed network environments is always challenging. There are two fundamental problems that constantly puzzle network administrators. First, how are devices securely identified? Second, how can devices initialize trust between each other? This paper introduces a Secure Shell (SSH) public key-based device identification and trust initialization mechanism. By utilizing the widely deployed SSH protocol stacks, a device's SSH public keys, together with the attributes that describe the name, location, version, capabilities, etc., of a device, are registered as secure device identities. By exchanging the public key using SSH protocol itself, a circle of mutual trust can be initialized between managed devices and a central administration console. The mutual trust allows configuration data to be pushed from the administration console to all the trusted devices. It can also be used as the trust anchor to further initialize other type of trust.

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)0.005
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0030.005
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.002

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 head0.008
GPT teacher head0.265
Teacher spread0.257 · 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 designBench or experimental
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
Published2010
Admission routes1
Has abstractyes

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