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Ensure the Grid Interfaces and Payment Gateways Against Data Breach Attacks and Malware: Electric Vehicle Charging Station via Cybersecurity

2025· article· W7138997775 on OpenAlexaff
Paddabbai Chowdari Karanam, K. Gurunathan, Ganesamoorthy Pandian, Ramanan Sathiaseelan, Koushik Lingam, Shiva Shankar Mummidi

Bibliographic record

Venuenot available
Typearticle
Language
FieldEngineering
TopicSmart Grid Security and Resilience
Canadian institutionsHumber Polytechnic
Fundersnot available
KeywordsAuthentication (law)MalwarePaymentGridElectric vehicleSmart gridSoftware deploymentEncryption

Abstract

fetched live from OpenAlex

Electric Vehicle Charging Stations (EVCS) deployment has been growing exponentially due to the fast integration of Electric Vehicles (EVs). These stations do not just handle the transfer of energy between the power grid and EVs, but also the delicate payment and user authentication procedures. Therefore, EVCS systems are becoming appealing to cyber attackers that want to take advantage of the existing vulnerabilities at grid interfaces and payment points. Privacy integrity and availability of such essential infrastructures are seriously threatened by data-leaking events, malware infection, ransomware and distributed denial-of-service (DDoS) attacks. “we have to have a substantial amount of control over the data, both on an information network and the physical operations themselves.” —these breaches can also result in theft or alteration of financial data, and mismanagement of charging operations, energy theft and potentially destabilization of the grid. This study prescribes a holistic cybersecurity architecture including solutions for both grid communication links, as well as payment processing subsystems at EVCS locations. The method combines multi- factor authentication, anti-malware, malware detection/prevention, and secure communication protocols for an end-to-end secure operation. To avoid unauthorized access or manipulation in the command of energy transfer, encrypted communication with mutual authentication is implemented on grid interfaces. Tokenization, two-factor authentication and process of identification and verification for advanced fraud detection are methods that protect the payment gateways and financial transactions. Malware is combated with constant system monitoring, signature-based scanning and AI-based anomaly detection that can detect zero-day threats by identifying when things are not operating as they should be. Also, the solution includes firmware integrity check to prevent malicious attacks and uses segmentation to separate the payment network from the grid control systems, allowing the two to be isolated from each other – thus reducing the likelihood of cross system compromise. The experimental results are obtained on a simulated EVCS network with real-world charging and payment transactions data for different cyberattack scenarios, such as man-in-the-middle, SQL injection, phishing-based credential theft, and malware injection. Experiments showed a 97.8% detection rate, a 90% of the payment fraud attempts prevented and a very low overhead (<50ms) to normal charging or payment operations. The proposed framework provides a mechanism to make the infrastructure of EVCS a layered defense system that can address operation and finance security issues together. The project increases trust in EV uptake, strengthens regulatory adherence to data protection laws, and erects a resilient shield against advancing cyber threats within an expanding smart mobility environment. This study illustrates that, in addition to safeguarding monetary transactions, active cybersecurity is necessary to ensure the security of the operation of power systems linked to EV charging systems.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.007
Open science0.0010.003
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.012
GPT teacher head0.242
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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), 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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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