TY - GEN
T1 - SwipeVLock: A Supervised Unlocking Mechanism Based on Swipe Behavior on Smartphones
AU - Li, Wenjuan
AU - Tan, Jiao
AU - Meng, Weizhi
AU - Wang, Yu
AU - Li, Jing
N1 - Funding Information:
We would like to thank the participants for their hard work in the user study. This work was partially supported by National Natural Science Foundation of China (No. 61802077).
Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
PY - 2019
Y1 - 2019
N2 - Smartphones have become a necessity in people’s daily lives, and changed the way of communication at any time and place. Nowadays, mobile devices especially smartphones have to store and process a large amount of sensitive information, i.e., from personal to financial and professional data. For this reason, there is an increasing need to protect the devices from unauthorized access. In comparison with the traditional textual password, behavioral authentication can verify current users in a continuous way, which can complement the existing authentication mechanisms. With the advanced capability provided by current smartphones, users can perform various touch actions to interact with their devices. In this work, we focus on swipe behavior and aim to design a machine learning-based unlock scheme called SwipeVLock, which verifies users based on their way of swiping the phone screen with a background image. In the evaluation, we measure several typical supervised learning algorithms and conduct a user study with 30 participants. Our experimental results indicate that participants could perform well with SwipeVLock, i.e., with a success rate of 98% in the best case.
AB - Smartphones have become a necessity in people’s daily lives, and changed the way of communication at any time and place. Nowadays, mobile devices especially smartphones have to store and process a large amount of sensitive information, i.e., from personal to financial and professional data. For this reason, there is an increasing need to protect the devices from unauthorized access. In comparison with the traditional textual password, behavioral authentication can verify current users in a continuous way, which can complement the existing authentication mechanisms. With the advanced capability provided by current smartphones, users can perform various touch actions to interact with their devices. In this work, we focus on swipe behavior and aim to design a machine learning-based unlock scheme called SwipeVLock, which verifies users based on their way of swiping the phone screen with a background image. In the evaluation, we measure several typical supervised learning algorithms and conduct a user study with 30 participants. Our experimental results indicate that participants could perform well with SwipeVLock, i.e., with a success rate of 98% in the best case.
KW - Behavioral biometric
KW - Smartphone security
KW - Swipe behavior
KW - Touch action
KW - User authentication
UR - http://www.scopus.com/inward/record.url?scp=85072874012&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-30619-9_11
DO - 10.1007/978-3-030-30619-9_11
M3 - Conference article published in proceeding or book
AN - SCOPUS:85072874012
SN - 9783030306182
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 140
EP - 153
BT - Machine Learning for Cyber Security - 2nd International Conference, ML4CS 2019, Proceedings
A2 - Chen, Xiaofeng
A2 - Huang, Xinyi
A2 - Zhang, Jun
PB - Springer Verlag
T2 - 2nd International Conference on Machine Learning for Cyber Security, ML4CS 2019
Y2 - 19 September 2019 through 21 September 2019
ER -