TY - GEN
T1 - WiAU
T2 - 15th Annual IEEE International Conference on Sensing, Communication, and Networking, SECON 2018
AU - Lin, Chi
AU - Hu, Jiaye
AU - Sun, Yu
AU - Ma, Fenglong
AU - Wang, Lei
AU - Wu, Guowei
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/6/26
Y1 - 2018/6/26
N2 - The ubiquitous and fine-grained features of WiFi signals make it promising for achieving device-free authentication. However, traditional methods suffer from drawbacks such as sensitivity to environmental dynamics, low accuracy, long delay, etc. In this paper, we introduce how to validate human identity using the ubiquitous WiFi signals. We develop WiAU, a device-free authentication system which only utilizes a Commodity Off-The-Shelf (COTS) router and a laptop. We describe the constitutions of WiAU and how it works in detail. Through collecting channel state information (CSI) profiles, WiAU automatically segments coherent activities and walking gait using an automatic segment algorithm (ASA). Then, a ResNet algorithm with two dedicated loss functions is designed to validate legal users and recognize illegal ones. Finally, experiments are conducted from different scenes to highlight the superiorities of WiAU in terms of high accuracy, short delay and robustness, revealing that WiAU has an accuracy of over 98% in recognizing human identity and human activities respectively.
AB - The ubiquitous and fine-grained features of WiFi signals make it promising for achieving device-free authentication. However, traditional methods suffer from drawbacks such as sensitivity to environmental dynamics, low accuracy, long delay, etc. In this paper, we introduce how to validate human identity using the ubiquitous WiFi signals. We develop WiAU, a device-free authentication system which only utilizes a Commodity Off-The-Shelf (COTS) router and a laptop. We describe the constitutions of WiAU and how it works in detail. Through collecting channel state information (CSI) profiles, WiAU automatically segments coherent activities and walking gait using an automatic segment algorithm (ASA). Then, a ResNet algorithm with two dedicated loss functions is designed to validate legal users and recognize illegal ones. Finally, experiments are conducted from different scenes to highlight the superiorities of WiAU in terms of high accuracy, short delay and robustness, revealing that WiAU has an accuracy of over 98% in recognizing human identity and human activities respectively.
UR - https://www.scopus.com/pages/publications/85050224979
UR - https://www.scopus.com/inward/citedby.url?scp=85050224979&partnerID=8YFLogxK
U2 - 10.1109/SAHCN.2018.8397108
DO - 10.1109/SAHCN.2018.8397108
M3 - Conference contribution
AN - SCOPUS:85050224979
T3 - 2018 15th Annual IEEE International Conference on Sensing, Communication, and Networking, SECON 2018
SP - 1
EP - 9
BT - 2018 15th Annual IEEE International Conference on Sensing, Communication, and Networking, SECON 2018
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 11 June 2018 through 13 June 2018
ER -