  This paper investigates whether computer usage profiles comprised of
process-, network-, mouse-, and keystroke-related events are unique and
consistent over time in a naturalistic setting, discussing challenges and
opportunities of using such profiles in applications of continuous
authentication. We collected ecologically-valid computer usage profiles from 31
MS Windows 10 computer users over 8 weeks and submitted this data to
comprehensive machine learning analysis involving a diverse set of online and
offline classifiers. We found that: (i) profiles were mostly consistent over
the 8-week data collection period, with most (83.9%) repeating computer usage
habits on a daily basis; (ii) computer usage profiling has the potential to
uniquely characterize computer users (with a maximum F-score of 99.90%); (iii)
network-related events were the most relevant features to accurately recognize
profiles (95.69% of the top features distinguishing users were
network-related); and (iv) binary models were the most well-suited for profile
recognition, with better results achieved in the online setting compared to the
offline setting (maximum F-score of 99.90% vs. 95.50%).
