Non-obtrusive Imposter Detection During Online Assessments

Cheating during online assessments is a major challenge faced by test administering institutions. Impersonation is the most common fraudulent act. Since impersonation can be resorted to at any stage of an online test, conventional access control at initial login is ineffective. Hence, the need for continuous authentication. Although technology is available to monitor candidates continuously while they take online tests, there are several constraints that limit their deployment. This paper presents a non-obtrusive, easily deployable, and effective continuous authentication system that could detect impersonation during online assessments. It uses a popular class of behavioural biometrics called ‘keystroke dynamics’ which are basically precision time measurements that can be made latently without the aid of any additional accessories. Authentication is performed using a novel parameter called the ‘angle of departure’ together with the ‘pause durations’, both computed from the biometrics captured during the online test and those captured during a mock test conducted as part of the candidate registration process. The underlying concept is presented in detail. Details of experiments conducted to establish proof of the concept are given. These include roleplays conducted for the acquisition of biometrics and simulations of genuine test taking as well as impersonation. The performance of the system is evaluated using both traditional metrics as well as recently established metrics that are considered as being more suitable for assessing continuous authentication systems. Analysis of the results indicates the effectiveness of the use of the angle of departure and pause durations for continuous authentication of candidates during online tests.