Could We Distinguish Child Users from Adults Using Keystroke Dynamics?
This addresses the problem of protecting children from internet threats by identifying them based on typing behavior, but it is incremental as it builds on existing keystroke dynamics methods.
The study tackled the problem of distinguishing child users from adults using keystroke dynamics to protect children from online threats, achieving equal error rates of less than 10% in distinguishing them, though error rates increased significantly with impostors.
Significant portion of contemporary computer users are children, who are vulnerable to threats coming from the Internet. To protect children from such threats, in this study, we investigate how successfully typing data can be used to distinguish children from adults. For this purpose, we collect a dataset comprising keystroke data of 100 users and show that distinguishing child Internet users from adults is possible using Keystroke Dynamics with equal error rates less than 10 percent. However the error rates increase significantly when there are impostors in the system.