Awan, Maaz AliDalveren, YaserCatak, Ferhat OzgurKara, AliDepartment of Electrical & Electronics Engineering2024-07-052024-07-05202302079-929210.3390/electronics122449142-s2.0-85180709360https://doi.org/10.3390/electronics12244914https://hdl.handle.net/20.500.14411/2316Catak, Ferhat Ozgur/0000-0002-2434-9966; Kara, Ali/0000-0002-9739-7619; Dalveren, Yaser/0000-0002-9459-0042Smart 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.eninfo:eu-repo/semantics/openAccessradio frequency fingerprintingmachine learningdeep learningsoftware-defined radioInternet of Thingscybersecuritysmart citysmart gridArticleQ21224WOS:001130618800001