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Measuring the security posture of IEC 61850 substations with redundancy against zero day attacks

2017· article· en· W2798628810 on OpenAlexaff
Onur Duman, Mengyuan Zhang, Lingyu Wang, Mourad Debbabi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSmart Grid Security and Resilience
Canadian institutionsConcordia University
Fundersnot available
KeywordsIEC 61850Redundancy (engineering)Computer scienceAutomationCircuit breakerReliability engineeringMetric (unit)Computer securityEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

As one of the most critical components of the smart grid, substations are responsible for distributing the energy to end users. According to the substation automation standard, IEC 61850-90-4, substations contain highly complex and interconnected networks, which are typically designed with redundancy to improve the availability in case of failures. The redundancy usually takes the form of multiple subsystems with identical functionality, such that one failed subsystem would not affect the normal operation of the entire substation. However, we show that such redundant subsystems are not always effective against malicious attacks, because, unlike natural faults, attackers may deliberately target the weakest link, i.e., common vulnerabilities found in multiple subsystems. In this paper, we first present a detailed substation configuration designed based on IEC 61850 and industrial practices. We then devise a novel security metric, namely, the factor of security, to measure the effectiveness of redundant subsystems against unknown zero day attacks. We apply the metric to two concrete attacks scenarios, time delay attack, and the tripping circuit breakers attack. Finally, we evaluate the metric through simulations.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.440
Threshold uncertainty score0.348

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 head0.014
GPT teacher head0.218
Teacher spread0.204 · 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 designObservational
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

Citations6
Published2017
Admission routes1
Has abstractyes

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