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Record W1926089503 · doi:10.1109/ecce.2015.7309828

New smart-grid operation-based network access control

2015· article· en· W1926089503 on OpenAlexaff
Herman Cheung, Cungang Yang, Helen Cheung

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSmart Grid Security and Resilience
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsSmart gridAccess controlComputer scienceGridComputer networkMicrogridComputer securityMetering modeAccess networkDistributed computingControl (management)Engineering

Abstract

fetched live from OpenAlex

Changes due to increasing use of equipment with communication capability in electricity distribution systems, development of microgrids, government-imposed electricity-market open access competitions, etc., have let electricity utilities in a greater reliance on communication networks for smart-grid operations that include monitoring, protection, control, and time-of-use metering. This paper presents a new smart-grid network access control strategy and a new operation-based access model in order to increase the grid-access security and grid-operation efficiency. The new access model extends the network access control from a traditional single security domain to multiple domains specifically designed for interconnected microgrids. A security policy to simplify power-grid network security administrations is proposed, the authorization is independently defined and separated from policy representations as well as implementation mechanisms, and digital credential is introduced to establish trust and role assignments for users in different microgrid domains. The proposed smart-grid operation-based network access control has significant advantages over the standard role-based access control for application on smart-grid operations. This paper presents case studies for illustrating this new smart-grid operation-based network access controls.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.970
Threshold uncertainty score0.479

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 science0.0000.000
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 head0.019
GPT teacher head0.236
Teacher spread0.217 · 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

Citations8
Published2015
Admission routes1
Has abstractyes

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