2,583 research outputs found
Sequential Keystroke Behavioral Biometrics for Mobile User Identification via Multi-view Deep Learning
With the rapid growth in smartphone usage, more organizations begin to focus
on providing better services for mobile users. User identification can help
these organizations to identify their customers and then cater services that
have been customized for them. Currently, the use of cookies is the most common
form to identify users. However, cookies are not easily transportable (e.g.,
when a user uses a different login account, cookies do not follow the user).
This limitation motivates the need to use behavior biometric for user
identification. In this paper, we propose DEEPSERVICE, a new technique that can
identify mobile users based on user's keystroke information captured by a
special keyboard or web browser. Our evaluation results indicate that
DEEPSERVICE is highly accurate in identifying mobile users (over 93% accuracy).
The technique is also efficient and only takes less than 1 ms to perform
identification.Comment: 2017 Joint European Conference on Machine Learning and Knowledge
Discovery in Database
- …