Online Binary Models are Promising for Distinguishing Temporally Consistent Computer Usage Profiles

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

1 Scopus Citations
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Author(s)

  • Luiz Giovanini
  • Fabrício Ceschin
  • Mirela Silva
  • Aokun Chen
  • Ramchandra Kulkarni
  • Sanjay Banda
  • Madison Lysaght
  • Heng Qiao
  • Nikolaos Sapountzis
  • Ruimin Sun
  • Brandon Matthews
  • André Grégio
  • Daniela Oliveira

Detail(s)

Original languageEnglish
Pages (from-to)412-423
Journal / PublicationIEEE Transactions on Biometrics, Behavior, and Identity Science
Volume4
Issue number3
Online published2 Jun 2022
Publication statusPublished - Jul 2022
Externally publishedYes

Abstract

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%).

Research Area(s)

  • Analytical models, Behavioral sciences, Biological system modeling, Biometrics (access control), Computational modeling, Computer security, Computer user profiling, continuous authentication, machine learning, time series analysis, user study

Citation Format(s)

Online Binary Models are Promising for Distinguishing Temporally Consistent Computer Usage Profiles. / Giovanini, Luiz; Ceschin, Fabrício; Silva, Mirela et al.
In: IEEE Transactions on Biometrics, Behavior, and Identity Science, Vol. 4, No. 3, 07.2022, p. 412-423.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review