R2BN: An Adaptive Model for Keystroke-Dynamics-Based Educational Level Classification

Ioannis Tsimperidis, Paul D. Yoo, Kamal Taha, Alexios Mylonas, Vasilis Katos

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


Over the past decade, keystroke-based pattern recognition techniques, as a forensic tool for behavioral biometrics, have gained increasing attention. Although a number of machine learning-based approaches have been proposed, they are limited in terms of their capability to recognize and profile a set of an individual's characteristics. In addition, up to today, their focus was primarily gender and age, which seem to be more appropriate for commercial applications (such as developing commercial software), leaving out from research other characteristics, such as the educational level. Educational level is an acquired user characteristic, which can improve targeted advertising, as well as provide valuable information in a digital forensic investigation, when it is known. In this context, this paper proposes a novel machine learning model, the randomized radial basis function network, which recognizes and profiles the educational level of an individual who stands behind the keyboard. The performance of the proposed model is evaluated by using the empirical data obtained by recording volunteers' keystrokes during their daily usage of a computer. Its performance is also compared with other well-referenced machine learning models using our keystroke dynamic datasets. Although the proposed model achieves high accuracy in educational level prediction of an unknown user, it suffers from high computational cost. For this reason, we examine ways to reduce the time that is needed to build our model, including the use of a novel data condensation method, and discuss the tradeoff between an accurate and a fast prediction. To the best of our knowledge, this is the first model in the literature that predicts the educational level of an individual based on the keystroke dynamics information only.
Original languageEnglish
Pages (from-to)525 - 535
Number of pages11
JournalIEEE Transactions on Cybernetics
Issue number2
Early online date28 Sept 2018
Publication statusPublished - 1 Feb 2020


  • Biometrics (access control)
  • Computational modeling
  • Data analytics
  • Data models
  • forensic analysis
  • Hidden Markov models
  • Keyboards
  • keystroke dynamics
  • Machine learning
  • machine learning
  • Predictive models
  • user profiling


Dive into the research topics of 'R2BN: An Adaptive Model for Keystroke-Dynamics-Based Educational Level Classification'. Together they form a unique fingerprint.

Cite this