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Record W2906097308 · doi:10.1109/mie.2018.2874385

Physical-Layer Security for Industrial Wireless Control Systems: Basics and Future Directions

2018· article· en· W2906097308 on OpenAlexaff
Fei Pan, Zhibo Pang, Michele Luvisotto, Ming Xiao, Hong Wen

Bibliographic record

VenueIEEE Industrial Electronics Magazine · 2018
Typearticle
Languageen
FieldEngineering
TopicSmart Grid Security and Resilience
Canadian institutionsUniversity of Waterloo
FundersNational Key Research and Development Program of ChinaVINNOVANational Natural Science Foundation of China
KeywordsPHYWirelessPhysical layerComputer scienceWireless networkIndustrial control systemReliability (semiconductor)Computer networkControl (management)Telecommunications

Abstract

fetched live from OpenAlex

Wireless networks for industrial control systems are promising because of their reduced cost, flexible structure, and improved long-term reliability. However, wireless control systems are vulnerable to probing-free attacks (PFAs), which are not possible in wired control systems. Thus, wireless control systems must be made as secure as wired systems. Physical (PHY)-layer security technology (PHY-Sec) may be a new strategy for securing industrial wireless control systems. Among all PHY-Sec technologies, PHY-layer authentication is the first step for PHYSec in industrial wireless control systems. This article discusses the principles of PHY-Sec, its application to wireless control systems, and potential research directions.

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)0.003
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0030.008
Open science0.0010.001
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0050.001

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 head0.018
GPT teacher head0.236
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 designTheoretical or conceptual
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

Citations80
Published2018
Admission routes1
Has abstractyes

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