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Record W2560410907 · doi:10.1109/epec.2016.7771705

A study of resource-constrained cyber security planning for smart grid networks

2016· article· en· W2560410907 on OpenAlexaff
Md. Mahmud Hasan, Hussein T. Mouftah

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSmart Grid Security and Resilience
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsSCADAComputer scienceSmart gridCentralityNetwork topologyScheme (mathematics)Resource (disambiguation)Intrusion detection systemGridNode (physics)Distributed computingComputer securityComputer networkEngineering

Abstract

fetched live from OpenAlex

This paper studies cyber security planning issues in resource-constrained smart grid networks. In particular, it proposes a centrality-based trust system placement scheme for energy SCADA systems. It aims to utilize centrality measurements to improve cyber protection in resource-constrained scenarios. The role of centrality measurements is to rank nodes based on their importance in a network. Trust systems are specialized security devices that are capable of firewalling and network intrusion detection. They monitor both types of traffic, ingress and egress. They are mainly deployed to provide cyber protection to supervisory control and data acquisition (SCADA) systems. Due to budgetary constraints, only a selected number of nodes are equipped with trust systems. Those nodes are known as the trust nodes. The proposed scheme uses linear programming problem (LPP) formulations to select the trust nodes. Numerical results are obtained through case studies for the IEEE BUS 30 and BUS 57 test system topologies. The results reveal that the proposed scheme is capable of improving quality of cyber protection in resource-constrained scenarios.

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 score0.143
Threshold uncertainty score0.336

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 head0.013
GPT teacher head0.231
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 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

Citations2
Published2016
Admission routes1
Has abstractyes

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