Zhang, Junqing 
ORCID: 0000-0002-3502-2926, Shen, Guanxiong, Saad, Walid and Chowdhury, Kaushik
(2023)
Radio Frequency Fingerprint Identification for Device Authentication in the Internet of Things.
IEEE Communications Magazine, 61 (10).
pp. 110-115.
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manuscript - CM 2023 RFFI.pdf - Author Accepted Manuscript Download (2MB) | Preview |
Abstract
Device authentication of wireless devices at the physical layer could augment security enforcement before fully decoding packets. At the upper layers of the stack, this is conventionally handled by cryptographic schemes. However, the associated computing overhead may make such regular approaches unsuitable for the emerging class of Internet of Things devices, which are typically resource-constrained and embedded in areas that make them difficult to retrieve and reprogram. In contrast, radio frequency fingerprint identification (RFFI) exploits the unique hardware features as device identifiers at the physical layer. This article reviews both the state-of-the-art in engineered feature-based RFFI protocol design and advances in recent deep learning-based protocols, as well as a hybrid protocol that combines their advantages. Specifically, the hybrid approach leverages two methods: a more versatile distance-based classifier and an automatic feature extractor. This article also summarizes the goals of identification, verification and classification as applicable to RFFI, and how they can be achieved by the above protocols.
| Item Type: | Article |
|---|---|
| Divisions: | Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 07 Jul 2023 15:13 |
| Last Modified: | 30 Oct 2023 20:19 |
| DOI: | 10.1109/MCOM.003.2200974 |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3171530 |
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