Screen-based active user authentication

We investigate if screen-based recordings of computer interactions can be used for accurate active user authentication. A dataset of screen recordings of some PC interactions (MouseMoving, Typing, Scrolling, Other) of 21 users was collected and we ran a set of experiments to help our investigation. Low-dimensional feature vectors based on histogram of optical flows from each screen recording were used in our study. The first set of experiments investigated if these low-dimensional features can be used to recognize the type of interaction taking place in a particular recording and we found that linear SVM could succeed in achieving this with an accuracy of 91% on 5 test users. The second set of experiments explored if classifiers trained on different types of recordings can be used to verify user identity. The results indicated that SVMs trained on Scrolling recordings can achieve moderately low FAR and FRR error rates of 20.7% and 12.4%, respectively. These preliminary results indicate that further research in using screen-based recordings for active authentication can lead to a reliable soft cyber biometric.