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Record W4410580511 · doi:10.1007/s10207-025-01055-7

Cyber defense in OCPP for EV charging security risks

2025· article· en· W4410580511 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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueInternational Journal of Information Security · 2025
Typearticle
Languageen
FieldEngineering
TopicRadiation Effects in Electronics
Canadian institutionsnot available
FundersZarqa UniversityTrent UniversityNottingham Trent UniversityCentre International de Recherche sur le Cancer
KeywordsComputer securityComputer scienceCryptography

Abstract

fetched live from OpenAlex

Abstract The Open Charge Point Protocol (OCPP) is a widely adopted communication standard that enables vendor-independent communication between charging points and Electric Vehicle (EV) charging station management systems. OCPP has significant cyber risks in terms of weak authentication mechanisms and improper session handling, exposing it to potential EV charging-related security threats. The backward incompatibility of the recent version of OCPP also poses challenges in the seamless adoption of the protocol. This paper introduces a comprehensive cyber defense framework to mitigate the security risks associated with OCPP. Through a detailed analysis of its vulnerabilities, the framework proposes targeted enhancements and mitigation strategies to further strengthen its security. The results demonstrate that the proposed OCPP significantly enhances both security and performance, surpassing its predecessor and current state-of-the-art security solutions for EV charging.

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)0.001
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.120
Threshold uncertainty score0.500

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.006
GPT teacher head0.276
Teacher spread0.270 · 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