TY - GEN
T1 - Continuous User Verification via Respiratory Biometrics
AU - Liu, Jian
AU - Chen, Yingying
AU - Dong, Yudi
AU - Wang, Yan
AU - Zhao, Tiannming
AU - Yao, Yu Dong
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/7
Y1 - 2020/7
N2 - The ever-growing security issues in various mobile applications and smart devices create an urgent demand for a reliable and convenient user verification method. Traditional verification methods request users to provide their secrets (e.g., entering passwords and collecting fingerprints). We envision that the essential trend of user verification is to free users from active participation in the verification process. Toward this end, we propose a continuous user verification system, which re-uses the widely deployed WiFi infrastructure to capture the unique physiological characteristics rooted in user's respiratory motions. Different from the existing continuous verification approaches, posing dependency on restricted scenarios/user behaviors (e.g., keystrokes and gaits), our system can be easily integrated into any WiFi infrastructure to provide non-intrusive continuous verification. Specifically, we extract the respiration-related signals from the channel state information (CSI) of WiFi. We then derive the user-specific respiratory features based on the waveform morphology analysis and fuzzy wavelet transformation of the respiration signals. Additionally, a deep learning based user verification scheme is developed to identify legitimate users accurately and detect the existence of spoofing attacks. Extensive experiments involving 20 participants demonstrate that the proposed system can robustly verify/identify users and detect spoofers under various types of attacks.
AB - The ever-growing security issues in various mobile applications and smart devices create an urgent demand for a reliable and convenient user verification method. Traditional verification methods request users to provide their secrets (e.g., entering passwords and collecting fingerprints). We envision that the essential trend of user verification is to free users from active participation in the verification process. Toward this end, we propose a continuous user verification system, which re-uses the widely deployed WiFi infrastructure to capture the unique physiological characteristics rooted in user's respiratory motions. Different from the existing continuous verification approaches, posing dependency on restricted scenarios/user behaviors (e.g., keystrokes and gaits), our system can be easily integrated into any WiFi infrastructure to provide non-intrusive continuous verification. Specifically, we extract the respiration-related signals from the channel state information (CSI) of WiFi. We then derive the user-specific respiratory features based on the waveform morphology analysis and fuzzy wavelet transformation of the respiration signals. Additionally, a deep learning based user verification scheme is developed to identify legitimate users accurately and detect the existence of spoofing attacks. Extensive experiments involving 20 participants demonstrate that the proposed system can robustly verify/identify users and detect spoofers under various types of attacks.
UR - http://www.scopus.com/inward/record.url?scp=85088634400&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85088634400&partnerID=8YFLogxK
U2 - 10.1109/INFOCOM41043.2020.9155258
DO - 10.1109/INFOCOM41043.2020.9155258
M3 - Conference contribution
AN - SCOPUS:85088634400
T3 - Proceedings - IEEE INFOCOM
SP - 1
EP - 10
BT - INFOCOM 2020 - IEEE Conference on Computer Communications
T2 - 38th IEEE Conference on Computer Communications, INFOCOM 2020
Y2 - 6 July 2020 through 9 July 2020
ER -