DDoS detection in electric vehicle charging stations: A deep learning perspective via CICEV2023 dataset

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dc.contributor.authorAnlı, Yağız Alp
dc.contributor.authorÇıplak, Zeki
dc.contributor.authorSakalıuzun, Murat
dc.contributor.authorİzgü, Şeniz Zekiye
dc.contributor.authorYıldız, Kazım
dc.date.accessioned2024-09-20T06:17:56Z
dc.date.available2024-09-20T06:17:56Z
dc.date.issued2024
dc.departmentMeslek Yüksekokulu, Gedik Meslek Yüksekokulu, Bilgisayar Programcılığı Programıen_US
dc.description.abstractDistributed Denial of Service (DDoS) attacks have always been an important research topic in the field of information security. Regarding specialized infrastructures such as electric vehicle charging stations, detecting and preventing such attacks becomes even more critical. In the existing literature, most studies on DDoS attack detection focus on traditional methods that analyze network metrics such as network traffic, packet rates, and number of connections. These approaches attempt to detect attacks by identifying anomalies and irregularities in the network, but can have high error rates and fail to identify advanced attacks. Conversely though, detection methods based on system metrics use deeper and more insightful parameters such as processor utilization, memory usage, disk I/O operations, and system behavior. Such metrics provide a more detailed perspective than network-based approaches, allowing for more accurate detection of attacks. However, work in this area is not yet widespread enough further research and improvement are needed. The adoption of advanced system metrics-based methods can significantly improve the effectiveness of DDoS defense strategies, especially in next-generation and specialized infrastructures. This paper evaluates the applicability and effectiveness of Long ShortTerm Memory (LSTM) and Feed-Forward Network (FFN) in detecting DDoS attacks against electric vehicle charging stations through system metrics using CICEV2023 dataset. Experimental results show that the LSTM based model offers advantages in terms of speed and processing capacity, while the FFN is superior in terms of the accuracy.en_US
dc.identifier.doi10.1016/j.iot.2024.101343en_US
dc.identifier.issn2543-1536
dc.identifier.issn2542-6605
dc.identifier.scopus2-s2.0-85202537967en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://hdl.handle.net/11501/1528
dc.identifier.urihttps://doi.org/10.1016/j.iot.2024.101343
dc.identifier.volume28en_US
dc.identifier.wosWOS:001306184900001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorÇıplak, Zeki
dc.institutionauthorid0000-0002-0086-3223en_US
dc.language.isoenen_US
dc.publisherElsevier B.V.en_US
dc.relation.ispartofInternet of Things (Netherlands)en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectCICEV2023en_US
dc.subjectCyber Securityen_US
dc.subjectDDoS Attacken_US
dc.subjectDDoS Detectionen_US
dc.subjectElectric Vehiclesen_US
dc.subjectFeed-Forward Networken_US
dc.subjectInternet of Thingsen_US
dc.subjectLong Short-Term Memoryen_US
dc.titleDDoS detection in electric vehicle charging stations: A deep learning perspective via CICEV2023 dataseten_US
dc.typeArticleen_US

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