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Radio Frequency Fingerprint Identification for Device Authentication in the Internet of Things

Authors :
Zhang, Junqing
Shen, Guanxiong
Saad, Walid
Chowdhury, Kaushik
Source :
IEEE Communications Magazine; 2023, Vol. 61 Issue: 10 p110-115, 6p
Publication Year :
2023

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 re-program. 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.

Details

Language :
English
ISSN :
01636804 and 15581896
Volume :
61
Issue :
10
Database :
Supplemental Index
Journal :
IEEE Communications Magazine
Publication Type :
Periodical
Accession number :
ejs64401568
Full Text :
https://doi.org/10.1109/MCOM.003.2200974