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Record W3030363216 · doi:10.1109/tii.2020.2998479

Blockchain for Cybersecurity in Smart Grid: A Comprehensive Survey

2020· article· en· W3030363216 on OpenAlexaff
Peng Zhuang, Talha Zamir, Hao Liang

Bibliographic record

VenueIEEE Transactions on Industrial Informatics · 2020
Typearticle
Languageen
FieldComputer Science
TopicBlockchain Technology Applications and Security
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsBlockchainSmart gridComputer securityComputer scienceDistributed ledgerGridEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

Blockchain is an immutable type of distributed ledger that is capable of storing data without relying on a third party. Blockchain technology has attracted significant interest in research areas, including its application in the smart grid for cybersecurity. Although significant efforts have been devoted to utilizing blockchain in the smart grid for cybersecurity, there is a lack of comprehensive survey on blockchain in the smart grid for cybersecurity in both application and technological perspectives. To fill this gap, we conducted a comprehensive survey on blockchain for smart gird cybersecurity. This conducted survey presents the latest insights of ideas, architectures, and techniques of implementation that are relevant to blockchain's application in the smart grid for cybersecurity. This article aims at providing helpful guidance and reference for future research efforts specific to blockchain for cybersecurity in the smart grid.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.008
Science and technology studies0.0010.001
Scholarly communication0.0020.006
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.082
GPT teacher head0.271
Teacher spread0.190 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations222
Published2020
Admission routes1
Has abstractyes

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