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Behavioral biometrics are key components in the landscape of research incontinuous and active user authentication. However, there is a lack of largedatasets with multiple activities, such as typing, gait and swipe performed bythe same person. Furthermore, large datasets with multiple activities performedon multiple devices by the same person are non-existent. The difficulties ofprocuring devices, participants, designing protocol, secure storage andon-field hindrances may have contributed to this scarcity. The availability ofsuch a dataset is crucial to forward the research in behavioral biometrics asusage of multiple devices by a person is common nowadays. Through this paper,we share our dataset, the details of its collection, features for each modalityand our findings of how keystroke features vary across devices. We havecollected data from 117 subjects for typing (both fixed and free text), gait(walking, upstairs and downstairs) and touch on Desktop, Tablet and Phone. Thedataset consists a total of about: 3.5 million keystroke events; 57.1 milliondata-points for accelerometer and gyroscope each; 1.7 million data-points forswipes; and enables future research to explore previously unexplored directionsin inter-device and inter-modality biometrics. Our analysis on keystrokesreveals that in most cases, keyhold times are smaller but inter-key latenciesare larger, on hand-held devices when compared to desktop. We also present;detailed comparison with related datasets; possible research directions withthe dataset; and lessons learnt from the data collection.

Insights from BB-MAS -- A Large Dataset for Typing, Gait and Swipes of the Same Person on Desktop, Tablet and Phone