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Record W2896797107 · doi:10.1109/bsc.2018.8494696

Evaluation of Industrial Firewall Performance Issues in Automation and Control Networks

2018· article· en· W2896797107 on OpenAlexaff
Davison Zvabva, Pavol Zavarsky, Sergey Butakov, John Luswata

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSmart Grid Security and Resilience
Canadian institutionsConcordia University of Edmonton
Fundersnot available
KeywordsComputer scienceFirewall (physics)ModbusComputer networkIndustrial control systemNetwork securityComputer securitySCADAApplication firewallNetwork packetNetwork Access ProtectionStateful firewallControl (management)Communications protocolEngineering

Abstract

fetched live from OpenAlex

The IEC 62443 security standards introduce the concepts of zones, conduits, and security levels as a way of segmenting and isolating the sub-systems of an industrial control network. Network segmentation logically partition the control network into separate communication zones to restrict unnecessary flow of traffic between zones of different trust level. Firewalls with deep packet inspection capabilities for filtering industrial control protocols are indispensable elements in implementing important security principles, standards, and best practices of IEC 62443. While partitioning of the industrial control network and placement of multiple firewalls at various locations provides defense in-depth against cyber-attacks, it is important to consider the impact of these firewalls on nodes distributing time critical communications. This paper attempts to (i) study network performance impact introduced by the implementation of multiple firewalls in Modbus TCP/IP industrial control networks following IEC 62443 security standards and (ii) evaluate if time constraint requirements for communications are achievable. The results reveal that the latency and jitters introduced by multilayered firewalls makes it challenging to achieve real-time communications in some industrial applications when strict IEC 62443 security standards are followed.

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)0.001
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: Empirical
Teacher disagreement score0.439
Threshold uncertainty score0.136

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.020
GPT teacher head0.247
Teacher spread0.226 · 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

Citations11
Published2018
Admission routes1
Has abstractyes

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