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Record W3209743353 · doi:10.1109/tsmc.2021.3121205

A Key-Agreement Scheme for Cyber–Physical Systems

2021· article· en· W3209743353 on OpenAlexafffund
Walter Lúcia, Amr Youssef

Bibliographic record

VenueIEEE Transactions on Systems Man and Cybernetics Systems · 2021
Typearticle
Languageen
FieldEngineering
TopicSmart Grid Security and Resilience
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCyber-physical systemKey (lock)Computer scienceScheme (mathematics)CryptographyController (irrigation)State (computer science)ActuatorKey exchangePhysical systemDistributed computingTheoretical computer scienceComputer securityPublic-key cryptographyAlgorithmMathematicsArtificial intelligenceEncryption

Abstract

fetched live from OpenAlex

Traditional cryptographic approaches may not always be suitable for cyber–physical systems (CPSs). In this correspondence, we present a control-theoretic approach allowing a networked controller and a smart actuator of a stochastic CPS to agree on a common secret key without resorting to classical cryptographic approaches. More precisely, by utilizing the asymmetry in the system model knowledge available to the control system defender and to the eavesdropper, we propose a key-agreement scheme utilizing a simultaneous input and state estimation algorithm. The validity of the proposed solution is shown through a numerical example.

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 categoriesMeta-epidemiology (narrow)
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 score0.609
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.013
GPT teacher head0.217
Teacher spread0.204 · 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.

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

Citations3
Published2021
Admission routes2
Has abstractyes

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