Keystroke dynamics for student authentication in online examinations

Biometrics are distinctive for each person, and can not be given away or hacked like a password. Keystroke dynamics is a behavioral biometric characteristic that can be used as a complementary authentication step [1]. In online examinations it is difficult to make sure that each student write their own work. Keystroke dynamics from these examinations could be used to detect attempted cheating. To detect cheating attempts, a Gaussian Mixture Models with Universal Background Model (GMM-UBM) was implemented, and tested on benchmark data set recorded from online examinations written in free text. The use of a Universal Background Model (UBM) allows students to be enrolled using a limited amount of data, making the sug- gested approach suitable for the use case. The use of a GMM-UBM resulted in an Equal Error Rate (ERR) of 5.4% and an accuracy of 94.5%