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Record W2937072220 · doi:10.1109/icassp.2019.8683696

Digitally Annealed Solution for the Vertex Cover Problem with Application in Cyber Security

2019· article· en· W2937072220 on OpenAlexaff
Mohammad Javad-Kalbasi, Keivan Dabiri, Shahrokh Valaee, Ali Sheikholeslami

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSmart Grid Security and Resilience
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPhasorVertex coverComputer scienceUnits of measurementCover (algebra)Vertex (graph theory)Computer securityState (computer science)Topology (electrical circuits)Distributed computingElectric power systemPower (physics)Theoretical computer scienceAlgorithmEngineeringGraphElectrical engineering

Abstract

fetched live from OpenAlex

Cyber attacks on the power systems can mislead the control center to produce incorrect state and topology estimate. State and topology attacks can have harmful impacts on the operation of a power system. The problem of placing secure phasor measurement units (PMUs) to detect these attacks has been studied in the literature. Specifically, it has been shown that placing secure PMUs to disable undetectable state and topology attacks can enhance the security of the system against cyber attacks. Placing secure PMUs is indeed the minimum vertex cover problem. Since the cost of deploying PMUs is high, it is important to place the secure PMUs efficiently in order to maximize the ability of detecting cyber attacks while reducing the costs. In this paper, we use Digital Annealer to solve the vertex cover problem. Digital Annealer is a hardware architecture for solving combinatorial optimization problems. We have performed numerous numerical experiments and noticed that our approach has an enhanced level of optimality compared to other well-known alternatives in the literature.

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 score0.191
Threshold uncertainty score0.174

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 head0.003
GPT teacher head0.182
Teacher spread0.178 · 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

Citations19
Published2019
Admission routes1
Has abstractyes

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