Touch-based interaction dataset for user behavioral analysis in mobile devices.

As smartphones and tablets are increasingly integrated into numerous aspects of everyday life, security on mobile devices is becoming important. Although facial recognition or fingerprint scanning are commonly employed to verify user identity, they are not always suitable for on-demand validation during specific moments of interaction. This limitation has motivated the search for alternative solutions, particularly those based on behavioral biometrics, as they enable the capture and analysis of unique interaction patterns without disrupting the user experience. This paper describes a dataset comprising touch-based interactions collected from 37 distinct users within a controlled ad-hoc scenario designated for authentication purposes. The dataset includes a wide range of touch gestures, from single-touch events (e.g., tap, swipe or pan) to multi-touch interactions (e.g., pinch or rotate), which can be utilized to extract individual behavioral patterns from user-device interactions, thus supporting further research. In particular, the data acquisition process is thoroughly described, so that raw data can be appropriately understood, as well as those features that were extracted to carry out our own research. Apart from the data being available on a public repository, we also include some base code that can help other researchers to handle raw data and extract the aforementioned features, so that they can adapt or even extend them according to their own needs.