Online Binary Models are Promising for Distinguishing Temporally Consistent Computer Usage Profiles
Luiz Giovanini, Fabrício Ceschin, Mirela Silva, Aokun Chen, Ramchandra Kulkarni, Sanjay Banda, Madison Lysaght, Heng Qiao, Nikolaos Sapountzis, Ruimin Sun, Brandon Matthews, Dapeng Oliver Wu, André Grégio, Daniela Oliveira
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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%).