Detection of cyber attacks in electric vehicle charging systems using a remaining useful life generative adversarial network

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dc.contributor.authorTanyildiz, Hayriye
dc.contributor.authorSahin, Canan Batur
dc.contributor.authorDinler, Ozlem Batur
dc.contributor.authorMigdady, Hazem
dc.contributor.authorSaleem, Kashif
dc.contributor.authorSmerat, Aseel
dc.contributor.authorAbualigah, Laith
dc.date.accessioned2026-06-19T06:39:35Z
dc.date.available2026-06-19T06:39:35Z
dc.date.issued2025
dc.departmentMalatya Turgut Özal Üniversitesi
dc.description.abstractCybersecurity attacks targeting electric vehicle supply equipment (EVSE) and the broader electric vehicle (EV) ecosystem have become an escalating concern with the increasing adoption of EVs and the growing connectivity of the infrastructure supporting them. The present research aims to contribute to continuing cybersecurity studies on electric vehicle charging stations. In line with this objective, this study proposes the remaining useful life (RUL) approach to demonstrate the potential impact of estimating the remaining time of a cyber attack on EVSE and what revolutionary changes it can bring to cyber security strategies using a generative adversarial network (GAN). By taking a proactive stance, the manuscript will increase security and reduce the economic and reputational losses associated with cyber incidents. Accurate RUL estimates present valuable information about the status of the EVSE infrastructure. Thus, informed decisions on maintenance and crew scheduling are taken. To test the technique's effectiveness, we assess this approach on attack scenarios, including network and host attacks on the EV charger (Electric Vehicle Supply Equipment-EVSE) in idle and charging states. Furthermore, we assess the prediction results of different deep learning models, such as gated recurrent units (GRUs), long short-term memory (LSTM), recurrent neural networks (RNNs), convolution neural networks (CNNs), multi-layer perceptron (MLP), and dense layer integrated with generative adversarial networks (GANs), using mean absolute error (MAE), root mean square error (RMSE), mean squared error (MSE), and R-squared (R2). Afterward, we compare the error measurements with models, such as hybrid GAN-LSTM, GAN-GRU, GAN-RNN, GAN-CNN, GAN-MLP, and GAN-Dense Layer. The GAN-GRU model exhibits the highest accuracy with the lowest MAE (0.0281). On the contrary, the GAN-CNN model displays the best overall performance concerning error consistency and variance explained. According to the results, integrating GAN into these architectures improves predictive accuracy and the model's ability to identify potential attacks in advance and decreases error rates.
dc.description.sponsorshipbuda University; King Saud University, Riyadh, Saudi Arabia [RSPD2024R697]
dc.description.sponsorshipOpen access funding provided by & Oacute;buda University. This work was supported by the King Saud University, Riyadh, Saudi Arabia, under Researchers Supporting Project number RSPD2024R697.
dc.identifier.doi10.1038/s41598-025-92895-9
dc.identifier.issn2045-2322
dc.identifier.issue1
dc.identifier.orcid0000-0001-8062-3301
dc.identifier.orcid0000-0002-2798-0104
dc.identifier.orcid0000-0002-2955-6761
dc.identifier.pmid40128270
dc.identifier.scopus2-s2.0-105000686897
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://doi.org/10.1038/s41598-025-92895-9
dc.identifier.urihttps://hdl.handle.net/20.500.12899/5689
dc.identifier.volume15
dc.identifier.wosWOS:001451257100026
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherNature Portfolio
dc.relation.ispartofScientific Reports
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20260612
dc.subjectElectric Vehicle Charging Systems
dc.subjectGenerative Artificial Intelligence
dc.subjectRemaining Useful Time
dc.subjectCyber Security
dc.titleDetection of cyber attacks in electric vehicle charging systems using a remaining useful life generative adversarial network
dc.typeArticle

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