TY - JOUR
T1 - GenePrint
T2 - Generic and Accurate Physical-Layer Identification for UHF RFID Tags
AU - Han, Jinsong
AU - Qian, Chen
AU - Yang, Panlong
AU - Ma, Dan
AU - Jiang, Zhiping
AU - Xi, Wei
AU - Zhao, Jizhong
N1 - Publisher Copyright:
© 1993-2012 IEEE.
PY - 2016/4
Y1 - 2016/4
N2 - Physical-layer identification utilizes unique features of wireless devices as their fingerprints, providing authenticity and security guarantee. Prior physical-layer identification techniques on radio frequency identification (RFID) tags require nongeneric equipments and are not fully compatible with existing standards. In this paper, we propose a novel physical-layer identification system, GenePrint, for UHF passive tags. The GenePrint prototype system is implemented by a commercial reader, a USRP-based monitor, and off-the-shelf UHF passive tags. Our solution is generic and completely compatible with the existing standard, EPCglobal C1G2 specification. GenePrint leverages the internal similarity among pulses of tags' RN16 preamble signals to extract a hardware feature as the fingerprint. We conduct extensive experiments on over 10 000 RN16 preamble signals from 150 off-the-shelf RFID tags. The results show that GenePrint achieves a high identification accuracy of 99.68%+. The feature extraction of GenePrint is resilient to various malicious attacks, such as the feature replay attack.
AB - Physical-layer identification utilizes unique features of wireless devices as their fingerprints, providing authenticity and security guarantee. Prior physical-layer identification techniques on radio frequency identification (RFID) tags require nongeneric equipments and are not fully compatible with existing standards. In this paper, we propose a novel physical-layer identification system, GenePrint, for UHF passive tags. The GenePrint prototype system is implemented by a commercial reader, a USRP-based monitor, and off-the-shelf UHF passive tags. Our solution is generic and completely compatible with the existing standard, EPCglobal C1G2 specification. GenePrint leverages the internal similarity among pulses of tags' RN16 preamble signals to extract a hardware feature as the fingerprint. We conduct extensive experiments on over 10 000 RN16 preamble signals from 150 off-the-shelf RFID tags. The results show that GenePrint achieves a high identification accuracy of 99.68%+. The feature extraction of GenePrint is resilient to various malicious attacks, such as the feature replay attack.
KW - Physical-layer identification
KW - radio frequency identification (RFID)
KW - similarity
UR - https://www.scopus.com/pages/publications/84922456022
U2 - 10.1109/TNET.2015.2391300
DO - 10.1109/TNET.2015.2391300
M3 - 文章
AN - SCOPUS:84922456022
SN - 1063-6692
VL - 24
SP - 846
EP - 858
JO - IEEE/ACM Transactions on Networking
JF - IEEE/ACM Transactions on Networking
IS - 2
M1 - 7029721
ER -