DDoS attack detection techniques in IoT networks: a survey

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dc.authorscopusidAli Ghaffari / 57197223215
dc.authorwosidAli Ghaffari / AAV-3651-2020
dc.contributor.authorPakmehr, Amir
dc.contributor.authorAßmuth, Andreas
dc.contributor.authorTaheri, Negar
dc.contributor.authorGhaffari, Ali
dc.date.accessioned2025-04-18T10:50:23Z
dc.date.available2025-04-18T10:50:23Z
dc.date.issued2024
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractThe Internet of Things (IoT) is a rapidly emerging technology that has become more valuable and vital in our daily lives. This technology enables connection and communication between objects and devices and allows these objects to exchange information and perform intelligent operations with each other. However, due to the scale of the network, the heterogeneity of the network, the insecurity of many of these devices, and privacy protection, it faces several challenges. In the last decade, distributed DDoS attacks in IoT networks have become one of the growing challenges that require serious attention and investigation. DDoS attacks take advantage of the limited resources available on IoT devices, which disrupts the functionality of IoT-connected applications and services. This article comprehensively examines the effects of DDoS attacks in the context of the IoT, which cause significant harm to existing systems. Also, this paper investigates several solutions to identify and deal with this type of attack. Finally, this study suggests a broad line of research in the field of IoT security, dedicated to examining how to adapt to current challenges and predicting future trends. © The Author(s) 2024.
dc.identifier.citationPakmehr, A., Aßmuth, A., Taheri, N., & Ghaffari, A. (2024). DDoS attack detection techniques in IoT networks: a survey. Cluster Computing, 27(10), 14637-14668.
dc.identifier.doi10.1007/s10586-024-04662-6
dc.identifier.endpage14668
dc.identifier.issn13867857
dc.identifier.issue10
dc.identifier.scopus2-s2.0-85199900143
dc.identifier.scopusqualityQ1
dc.identifier.startpage14637
dc.identifier.urihttp://dx.doi.org/10.1007/s10586-024-04662-6
dc.identifier.urihttps://hdl.handle.net/20.500.12713/7209
dc.identifier.volume27
dc.identifier.wosWOS:001276963700002
dc.identifier.wosqualityQ1
dc.indekslendigikaynakScopus
dc.institutionauthorGhaffari, Ali
dc.institutionauthoridAli Ghaffari / 0000-0001-5407-8629
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofCluster Computing
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectDDoS
dc.subjectInternet of Things
dc.subjectIntrusion Detection
dc.subjectMachine Learning
dc.titleDDoS attack detection techniques in IoT networks: a survey
dc.typeArticle

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