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Record W2606091117 · doi:10.5539/mas.v11n6p24

Industrial Network Security – A Critical Review

2017· review· en· W2606091117 on OpenAlexvenueno aff
Omar Salim Kidege, Stanislaw Maj

Bibliographic record

VenueModern Applied Science · 2017
Typereview
Languageen
FieldEngineering
TopicSmart Grid Security and Resilience
Canadian institutionsnot available
Fundersnot available
KeywordsSophisticationHackerComputer securityIndustrial control systemControl system securityComputer scienceProcess (computing)Risk analysis (engineering)Critical infrastructureState (computer science)Control (management)BusinessInformation securityNetwork security policySecurity service

Abstract

fetched live from OpenAlex

In advanced societies all aspects of commerce and industry are now based on networked IT systems. Failures of these systems have the potential to be extremely disruptive. The term Critical Infrastructure (CI) is used to define systems (private and public) considered vital to national interests whose interruption would have a debilitating effect on society. It is recognized cyber security threats to CIs range from malicious to state sponsored. The threats are typically continuous and evolving in sophistication. This paper is primarily focused on Process Control Networks (PCNs). PCNs are used as the basis of industrial process control in a wide range of applications (manufacturing, oil and gas, water etc.). Given the importance of this industrial sector there are a range of guidelines considered to be exemplars of best practice. However given the constantly evolving sophistication of hackers the true measure of security is penetration testing – not something that is practical in industrial systems.

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 score0.010
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.005
Science and technology studies0.0010.001
Scholarly communication0.0020.004
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0100.004

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 head0.148
GPT teacher head0.359
Teacher spread0.211 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

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