article.page.titleprefix
Deployment and Implementation Aspects of Radio Frequency Fingerprinting in Cybersecurity of Smart Grids

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dc.contributor.authorAwan, Maaz Ali
dc.contributor.authorDalveren, Yaser
dc.contributor.authorÇatak, Ferhat Özgür
dc.contributor.authorKara, Ali
dc.date.accessioned2024-01-16T07:35:41Z
dc.date.available2024-01-16T07:35:41Z
dc.date.issued2023-12-06
dc.descriptionOpen Access; Published by Electronics; https://doi.org/10.3390/electronics12244914; Maaz Ali Awan, Yaser Dalveren, Department of Electrical and Electronics Engineering, Atilim University, Ankara 06830, Turkey; Ferhat Ozgur Catak, Electrical Engineering and Computer Science, University of Stavanger, 4021 Rogaland, Norway; Ali Kara, Department of Electrical and Electronics Engineering, Gazi University, Ankara 06570, Turkey.
dc.description.abstractSmart grids incorporate diverse power equipment used for energy optimization in intelligent cities. This equipment may use Internet of Things (IoT) devices and services in the future. To ensure stable operation of smart grids, cybersecurity of IoT is paramount. To this end, use of cryptographic security methods is prevalent in existing IoT. Non-cryptographic methods such as radio frequency fingerprinting (RFF) have been on the horizon for a few decades but are limited to academic research or military interest. RFF is a physical layer security feature that leverages hardware impairments in radios of IoT devices for classification and rogue device detection. The article discusses the potential of RFF in wireless communication of IoT devices to augment the cybersecurity of smart grids. The characteristics of a deep learning (DL)-aided RFF system are presented. Subsequently, a deployment framework of RFF for smart grids is presented with implementation and regulatory aspects. The article culminates with a discussion of existing challenges and potential research directions for maturation of RFF.
dc.identifier.citationhttp://hdl.handle.net/20.500.14411/1961
dc.identifier.issn2079-9292
dc.identifier.urihttps://doi.org/10.3390/electronics12244914
dc.language.isoen
dc.publisherElectronics
dc.relation.ispartofseries12; 24
dc.subjectRadio frequency fingerprinting; machine learning; deep learning; software-defined radio; Internet of Things; cybersecurity; smart city; smart grid
dc.titleDeployment and Implementation Aspects of Radio Frequency Fingerprinting in Cybersecurity of Smart Grids
dc.typeArticle
dspace.entity.typeArticle

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