Conference paper · Book chapter
SwipeVLock: A Supervised Unlocking Mechanism Based on Swipe Behavior on Smartphones
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.
Language: | English |
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Publisher: | Springer |
Year: | 2019 |
Pages: | 140-153 |
Proceedings: | 2nd International Conference on Machine Learning for Cyber Security |
Series: | Lecture Notes in Computer Science (including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
Journal subtitle: | Second International Conference, Ml4cs 2019, Xi’an, China, September 19-21, 2019, Proceedings |
ISBN: | 3030306186 , 3030306194 , 9783030306182 and 9783030306199 |
ISSN: | 03029743 and 16113349 |
Types: | Conference paper and Book chapter |
DOI: | 10.1007/978-3-030-30619-9_11 |
ORCIDs: | Meng, Weizhi |