Next-Generation Intrusion Detection for IoT EVCS: Integrating CNN, LSTM, and GRU Models
DC Field | Value | Language |
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dc.contributor.author | Kilichev, Dusmurod | - |
dc.contributor.author | Turimov, Dilmurod | - |
dc.contributor.author | Kim, Wooseong | - |
dc.date.accessioned | 2024-03-24T01:30:18Z | - |
dc.date.available | 2024-03-24T01:30:18Z | - |
dc.date.issued | 2024-02 | - |
dc.identifier.issn | 2227-7390 | - |
dc.identifier.issn | 2227-7390 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/90796 | - |
dc.description.abstract | In the evolving landscape of Internet of Things (IoT) and Industrial IoT (IIoT) security, novel and efficient intrusion detection systems (IDSs) are paramount. In this article, we present a groundbreaking approach to intrusion detection for IoT-based electric vehicle charging stations (EVCS), integrating the robust capabilities of convolutional neural network (CNN), long short-term memory (LSTM), and gated recurrent unit (GRU) models. The proposed framework leverages a comprehensive real-world cybersecurity dataset, specifically tailored for IoT and IIoT applications, to address the intricate challenges faced by IoT-based EVCS. We conducted extensive testing in both binary and multiclass scenarios. The results are remarkable, demonstrating a perfect 100% accuracy in binary classification, an impressive 97.44% accuracy in six-class classification, and 96.90% accuracy in fifteen-class classification, setting new benchmarks in the field. These achievements underscore the efficacy of the CNN-LSTM-GRU ensemble architecture in creating a resilient and adaptive IDS for IoT infrastructures. The ensemble algorithm, accessible via GitHub, represents a significant stride in fortifying IoT-based EVCS against a diverse array of cybersecurity threats. | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | MDPI | - |
dc.title | Next-Generation Intrusion Detection for IoT EVCS: Integrating CNN, LSTM, and GRU Models | - |
dc.type | Article | - |
dc.identifier.wosid | 001169745200001 | - |
dc.identifier.doi | 10.3390/math12040571 | - |
dc.identifier.bibliographicCitation | MATHEMATICS, v.12, no.4 | - |
dc.description.isOpenAccess | Y | - |
dc.identifier.scopusid | 2-s2.0-85187259768 | - |
dc.citation.title | MATHEMATICS | - |
dc.citation.volume | 12 | - |
dc.citation.number | 4 | - |
dc.type.docType | Article | - |
dc.publisher.location | 스위스 | - |
dc.subject.keywordAuthor | convolutional neural network | - |
dc.subject.keywordAuthor | cybersecurity | - |
dc.subject.keywordAuthor | deep learning | - |
dc.subject.keywordAuthor | Edge-IIoTset | - |
dc.subject.keywordAuthor | electric vehicle charging station | - |
dc.subject.keywordAuthor | ensemble learning | - |
dc.subject.keywordAuthor | gated recurrent unit | - |
dc.subject.keywordAuthor | Internet of Things | - |
dc.subject.keywordAuthor | intrusion detection system | - |
dc.subject.keywordAuthor | long short-term memory | - |
dc.relation.journalResearchArea | Mathematics | - |
dc.relation.journalWebOfScienceCategory | Mathematics | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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