MétaCan
Menu
Back to cohort
Record W2338949789 · doi:10.1109/tsg.2015.2466611

A DER Attack-Mitigation Differential Game for Smart Grid Security Analysis

2015· article· en· W2338949789 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Transactions on Smart Grid · 2015
Typearticle
Languageen
FieldEngineering
TopicSmart Grid Security and Resilience
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaNational Science Foundation
KeywordsSmart gridCyber-physical systemComputer scienceResilience (materials science)Computer securityElectric power systemGridDistributed computingDifferential (mechanical device)Power (physics)EngineeringElectrical engineering

Abstract

fetched live from OpenAlex

Information and communication infrastructure will be extensively deployed to monitor and control electric power delivery components of today's power grid. While these cyber elements enhance a utility's ability to maintain physical stability, if a subset are compromised by adversaries, disruption may occur. In this paper, a novel framework based on the principles of differential games is proposed that demonstrates stealthy worst-case strategies for attackers to disrupt transient stability by leveraging control over distributed energy resources. We demonstrate that if the electric power utility is able to identify uncompromised components, countermeasures can exist that effectively reduce the impact of attack for a fixed time interval. Based on our results, we develop insights to construct safety margin recommendations for cyber-physical smart grid actuation elements that promote system resilience during a cyber attack.

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.578
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.022
GPT teacher head0.252
Teacher spread0.230 · 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