TY - GEN
T1 - GenePrint
T2 - 2013 21st IEEE International Conference on Network Protocols, ICNP 2013
AU - Ma, Dan
AU - Qian, Chen
AU - Li, Wenpu
AU - Han, Jinsong
AU - Zhao, Jizhong
PY - 2013
Y1 - 2013
N2 - Physical-layer identification utilizes unique features of wireless devices as their fingerprints, providing authenticity and security guarantee. Prior physical-layer identification techniques on RFID tags require non-generic 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 the 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 RFID tags require non-generic 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 the 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.
UR - https://www.scopus.com/pages/publications/84896802040
U2 - 10.1109/ICNP.2013.6733574
DO - 10.1109/ICNP.2013.6733574
M3 - 会议稿件
AN - SCOPUS:84896802040
SN - 9781479912704
T3 - Proceedings - International Conference on Network Protocols, ICNP
BT - Proceedings of the 2013 21st IEEE International Conference on Network Protocols, ICNP 2013
PB - IEEE Computer Society
Y2 - 7 October 2013 through 10 October 2013
ER -