TY - JOUR
T1 - Wrist in motion
T2 - A seamless context-aware continuous authentication framework using your clickings and typings
AU - Li, Borui
AU - Wang, Wei
AU - Gao, Yang
AU - Phoha, Vir V.
AU - Jin, Zhanpeng
N1 - Funding Information:
This work was partially supported by the National Science Foundation under Grant SaTC-1527795, Grant 1564046, and Grant 1840790. This article was recommended for publication by Associate Editor S. Schuckers upon evaluation of the reviewers' comments.
Publisher Copyright:
© 2020 IEEE.
PY - 2020/7
Y1 - 2020/7
N2 - In this paper, we propose enhanced continuous authentication by supplementing keystroke and mouse dynamics with wrist motion behaviors. Our method bridges the security gap when neither the mouse nor the keyboard is being used, such as during transitions from mouse to keyboard and vice versa, or during intermittent pauses when wrist movement is captured. Context-aware keystroke latency feature cell generation improves performance and solves latency fluctuation-different diagraphs have different latencies in different words. Based on two Random Forest Ensemble Classifiers (RFECs) recognizing the mouse and keystroke actions with corresponding wrist motions and one Sequential Sampling Analysis (SSA) or SSA Dynamic Trust Model (SSA-DTM), the identity of the user can be continuously verified no matter the operation mode-mouse clicking or keyboard typing. Experimental results, based on 44 subjects, show that the proposed approach can reach an FRR of 0.92% for genuine users and an FAR of 0 for attackers. The approach is shown to be more superior in efficient and timely authentications by making an authentication decision within only 35 mixed actions-mouse clicks or keystrokes, compared with conventional methods solely based on the mouse geometry and locomotion features or keystroke latency features.
AB - In this paper, we propose enhanced continuous authentication by supplementing keystroke and mouse dynamics with wrist motion behaviors. Our method bridges the security gap when neither the mouse nor the keyboard is being used, such as during transitions from mouse to keyboard and vice versa, or during intermittent pauses when wrist movement is captured. Context-aware keystroke latency feature cell generation improves performance and solves latency fluctuation-different diagraphs have different latencies in different words. Based on two Random Forest Ensemble Classifiers (RFECs) recognizing the mouse and keystroke actions with corresponding wrist motions and one Sequential Sampling Analysis (SSA) or SSA Dynamic Trust Model (SSA-DTM), the identity of the user can be continuously verified no matter the operation mode-mouse clicking or keyboard typing. Experimental results, based on 44 subjects, show that the proposed approach can reach an FRR of 0.92% for genuine users and an FAR of 0 for attackers. The approach is shown to be more superior in efficient and timely authentications by making an authentication decision within only 35 mixed actions-mouse clicks or keystrokes, compared with conventional methods solely based on the mouse geometry and locomotion features or keystroke latency features.
KW - Continuous authentication
KW - Free-text keystrokes
KW - Mouse dynamics
KW - Sequential sampling analysis
KW - Wrist motions
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U2 - 10.1109/TBIOM.2020.2997004
DO - 10.1109/TBIOM.2020.2997004
M3 - Article
AN - SCOPUS:85122048443
SN - 2637-6407
VL - 2
SP - 294
EP - 307
JO - IEEE Transactions on Biometrics, Behavior, and Identity Science
JF - IEEE Transactions on Biometrics, Behavior, and Identity Science
IS - 3
M1 - 9099884
ER -