TY - JOUR
T1 - A robust authentication scheme based on physical-layer phase noise fingerprint for emerging wireless networks
AU - Zhao, Caidan
AU - Huang, Minmin
AU - Huang, Lianfen
AU - Du, Xiaojiang
AU - Guizani, Mohsen
N1 - Publisher Copyright:
© 2017 Elsevier B.V.
PY - 2017/12/9
Y1 - 2017/12/9
N2 - The increasing demand for different types of wireless communication services and the advanced wireless technology has led to the presence of emerging networks. However, wireless communication allows attackers to send useless information into the network through the process of multi-hop transmission, which results in the forwarding of many invalid data and the consumption of energy. To address this, the network needs effective identity authentication. In this paper, we propose a new robust authentication algorithm based on the phase noise fingerprint of the physical-layer (PHY). Furthermore, we propose a security authentication scheme of combined PHY fingerprints to ensure the survivability of the network in the presence of attacks or intrusions. The experimental results show that the identification rate of the simple multiple kernel learning (SimpleMKL) reaches 98.25%, and the scheme is robust against malicious nodes and efficient with low computation and storage.
AB - The increasing demand for different types of wireless communication services and the advanced wireless technology has led to the presence of emerging networks. However, wireless communication allows attackers to send useless information into the network through the process of multi-hop transmission, which results in the forwarding of many invalid data and the consumption of energy. To address this, the network needs effective identity authentication. In this paper, we propose a new robust authentication algorithm based on the phase noise fingerprint of the physical-layer (PHY). Furthermore, we propose a security authentication scheme of combined PHY fingerprints to ensure the survivability of the network in the presence of attacks or intrusions. The experimental results show that the identification rate of the simple multiple kernel learning (SimpleMKL) reaches 98.25%, and the scheme is robust against malicious nodes and efficient with low computation and storage.
KW - Authentication scheme
KW - PHY security
KW - RF fingerprint
KW - Survivability
UR - http://www.scopus.com/inward/record.url?scp=85020668698&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85020668698&partnerID=8YFLogxK
U2 - 10.1016/j.comnet.2017.05.028
DO - 10.1016/j.comnet.2017.05.028
M3 - Article
AN - SCOPUS:85020668698
SN - 1389-1286
VL - 128
SP - 164
EP - 171
JO - Computer Networks
JF - Computer Networks
ER -