TY - GEN
T1 - Hand in Motion
T2 - 9th IEEE International Conference on Biometrics: Theory, Applications and Systems, BTAS 2018
AU - Li, Borui
AU - Wang, Wei
AU - Gao, Yang
AU - Phoha, Vir V.
AU - Jin, Zhanpeng
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/2
Y1 - 2018/7/2
N2 - Behavioral biometrics have been long used as a complementary method to the traditional one-time authentication system. Mouse dynamics, representing an individual's unique patterns of mouse operations, possess a great potential to bridge the security gap between two one-time authentications on the computer. In this paper, we propose a continuous authentication approach by combining the deviceindependent, angle-based mouse movement features and the wrist motion features. Based on a Random Forest Ensemble Classifier (RFEC) and the Sequential Sampling Analysis (SSA), the identity of the user can be continuously verified. Experimental results, based on 26 subjects, show that the proposed approach can reach the False Accept Rate (FAR) of 1.46% and 4.69% for impostors and intruders respectively and a False Reject Rate (FRR) of 0%. Moreover, the proposed approach is proven to be more effective in timely authentication (i.e., making an authentication decision within only 9 to 12 mouse clicks), compared with conventional methods solely based on the mouse geometry and locomotion features.
AB - Behavioral biometrics have been long used as a complementary method to the traditional one-time authentication system. Mouse dynamics, representing an individual's unique patterns of mouse operations, possess a great potential to bridge the security gap between two one-time authentications on the computer. In this paper, we propose a continuous authentication approach by combining the deviceindependent, angle-based mouse movement features and the wrist motion features. Based on a Random Forest Ensemble Classifier (RFEC) and the Sequential Sampling Analysis (SSA), the identity of the user can be continuously verified. Experimental results, based on 26 subjects, show that the proposed approach can reach the False Accept Rate (FAR) of 1.46% and 4.69% for impostors and intruders respectively and a False Reject Rate (FRR) of 0%. Moreover, the proposed approach is proven to be more effective in timely authentication (i.e., making an authentication decision within only 9 to 12 mouse clicks), compared with conventional methods solely based on the mouse geometry and locomotion features.
UR - https://www.scopus.com/pages/publications/85065430731
U2 - 10.1109/BTAS.2018.8698577
DO - 10.1109/BTAS.2018.8698577
M3 - 会议稿件
AN - SCOPUS:85065430731
T3 - 2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems, BTAS 2018
BT - 2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems, BTAS 2018
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 22 October 2018 through 25 October 2018
ER -