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Ramchandra Kulkarni

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Computer Users Have Unique Yet Temporally Inconsistent Computer Usage Profiles

May 20, 2021
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 temporally consistent 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 28 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) computer usage profiles have the potential to uniquely characterize computer users (with a maximum F-score of 99.94%); (ii) network-related events were the most useful features to properly recognize profiles (95.14% of the top features distinguishing users being network-related); (iii) user profiles were mostly inconsistent over the 8-week data collection period, with 92.86% of users exhibiting drifts in terms of time and usage habits; and (iv) online models are better suited to handle computer usage profiles compared to offline models (maximum F-score for each approach was 95.99% and 99.94%, respectively).

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