Dynamic Keystroke Technique for a Secure Authentication System based on Deep Belief Nets

Authors

  • Asia Othman Aljahdali College of Computer Science and Engineering, University of Jeddah, Saudi Arabia https://orcid.org/0000-0002-9013-9465
  • Fursan Thabit Department of Computer Engineering, Faculty of Engineering, Ege University, Turkey https://orcid.org/0000-0001-8021-1294
  • Hanan Aldissi College of Computer Science and Engineering, University of Jeddah, Saudi Arabia
  • Wafaa Nagro College of Computer Science and Engineering, University of Jeddah, Saudi Arabia
Volume: 13 | Issue: 3 | Pages: 10906-10915 | June 2023 | https://doi.org/10.48084/etasr.5841

Abstract

The rapid growth of electronic assessment in various fields has led to the emergence of issues such as user identity fraud and cheating. One potential solution to these problems is to use a complementary authentication method, such as a behavioral biometric characteristic that is unique to each individual. One promising approach is keystroke dynamics, which involves analyzing the typing patterns of users. In this research, the Deep Belief Nets (DBN) model is used to implement a dynamic keystroke technique for secure e-assessment. The proposed system extracts various features from the pressure-time measurements, digraphs (dwell time and flight time), trigraphs, and n-graphs, and uses these features to classify the user's identity by applying the DBN algorithm to a dataset collected from participants who typed free text using a standard QWERTY keyboard in a neutral state without inducing specific emotions. The DBN model is designed to detect cheating attempts and is tested on a dataset collected from the proposed e-assessment system using free text. The implementation of the DBN results in an error rate of 5% and an accuracy of 95%, indicating that the system is effective in identifying users' identities and cheating, providing a secure e-assessment approach.

Keywords:

keystroke dynamics, authentication, e-assessment, dwell time, flight time, Deep Belief Network (DBN)

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How to Cite

[1]
Aljahdali, A.O., Thabit, F., Aldissi, H. and Nagro , W. 2023. Dynamic Keystroke Technique for a Secure Authentication System based on Deep Belief Nets. Engineering, Technology & Applied Science Research. 13, 3 (Jun. 2023), 10906–10915. DOI:https://doi.org/10.48084/etasr.5841.

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