In the pursuit of advancing research in continuous user authentication, we introduce COUNT-OS-I and COUNT-OS-II, two distinct performance counter datasets from Windows operating systems, crafted to bolster research in continuous user authentication. Encompassing data from 63 computers and users, the datasets offer rich, real-world insights for developing and evaluating authentication models. COUNT-OS-I spans 26 users in an IT department, capturing 159 attributes across diverse hardware and software environments over 26 h on average per user. COUNT-OS-II, on the other hand, encompasses 37 users with identical system configurations, recording 218 attributes per sample over a 48-hour period. Both datasets utilize pseudonymization to safeguard user identities while maintaining data integrity and statistical accuracy. The well-balanced nature of the data, confirmed by comprehensive statistical analysis, positions these datasets as reliable benchmarks for the continuous user authentication domain. Through their release, we aim to empower the development of robust, real-world applicable authentication models, contributing to enhanced system security and user trust.
Keywords: Behavioural biometrics; Continuous authentication; Machine learning; Performance counters.
© 2023 The Author(s).