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Record W1974022228 · doi:10.1109/tpwrd.2015.2409887

Intrusion Evaluation of Communication Network Architectures for Power Substations

2015· article· en· W1974022228 on OpenAlexfundno aff
Rashiduzzaman Bulbul, Pingal Raj Sapkota, Chee‐Wooi Ten, Lingfeng Wang, Andrew Ginter

Bibliographic record

VenueIEEE Transactions on Power Delivery · 2015
Typearticle
Languageen
FieldEngineering
TopicSmart Grid Security and Resilience
Canadian institutionsnot available
FundersUniversity of CalgaryNational Science Foundation
KeywordsPower networkComputer scienceTelecommunications networkElectric power systemIntrusionElectrical engineeringPower (physics)EngineeringComputer networkReliability engineering

Abstract

fetched live from OpenAlex

Electronic elements of a substation control system have been recognized as critical cyberassets due to the increased complexity of the automation system that is further integrated with physical facilities. Since this can be executed by unauthorized users, the security investment of cybersystems remains one of the most important factors for substation planning and maintenance. As a result of these integrated systems, intrusion attacks can impact operations. This work systematically investigates the intrusion resilience of the ten architectures between a substation network and others. In this paper, two network architectures comparing computer-based boundary protection and firewall-dedicated virtual local-area networks are detailed, that is, architectures one and ten. A comparison on the remaining eight architecture models was performed. Mean time to compromise is used to determine the system operational period. Simulation cases have been set up with the metrics based on different levels of attackers’ strength. These results as well as sensitivity analysis show that implementing certain architectures would enhance substation network security.

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 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 score0.324
Threshold uncertainty score0.456

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.030
GPT teacher head0.261
Teacher spread0.231 · 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

Citations24
Published2015
Admission routes1
Has abstractyes

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