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Impact of the Open Charge Point Protocol Between the Electric Vehicle and the Fast Charging Station on the Cybersecurity of the Smart Grid

2022· article· en· W4308090755 on OpenAlexafffundabout
Kandarp Gandhi, Walid G. Morsi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSmart Grid Security and Resilience
Canadian institutionsOntario Tech University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSmart gridMicrogridComputer securityComputer scienceProtocol (science)Electric vehicleGridCyber-attackComputer networkPower (physics)Control (management)EngineeringElectrical engineering

Abstract

fetched live from OpenAlex

Cyber-physical security is becoming an important issue within the smart grid. The growth in sales of the electric vehicles (EVs) dictates the need for high-power fast charging stations (HP-FCS). The operation and control of such HP-FCS requires the integration of information and communication technologies (ICT), which makes such critical infrastructure prone to cyber-attacks and hence compromising the charging of the EVs in particular when operating in vehicle-to-grid mode. In Canada, the Open Charge Point Protocol (OCPP) is considered one of the most popular protocols used in HP-FCS. This charging protocol does not include built-in security measures against cyberattacks and hence it may represent an access point to the unauthorized users. In this paper, potential threats against HP-FCS that uses the OCPP are demonstrated through the cyberattacks applied to microgrid equipped with distributed renewable energy resources. Furthermore, the paper demonstrates how to implement a cyber-attack on such a microgrid when operating in an islanded mode while investigating the impact of such cyberphysical attacks on its components.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.408
Threshold uncertainty score0.566

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.011
GPT teacher head0.250
Teacher spread0.238 · 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

Citations17
Published2022
Admission routes3
Has abstractyes

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