  Biometrics is used to authenticate an individual based on physiological or
behavioral traits. Mouse dynamics is an example of a behavioral biometric that
can be used to perform continuous authentication as protection against security
breaches. Recent research on mouse dynamics has shown promising results in
identifying users; however, it has not yet reached an acceptable level of
accuracy. In this paper, an empirical evaluation of different classification
techniques is conducted on a mouse dynamics dataset, the Balabit Mouse
Challenge dataset. User identification is carried out using three mouse
actions: mouse move, point and click, and drag and drop. Verification and
authentication methods are conducted using three machine-learning classifiers:
the Decision Tree classifier, the K-Nearest Neighbors classifier, and the
Random Forest classifier. The results show that the three classifiers can
distinguish between a genuine user and an impostor with a relatively high
degree of accuracy. In the verification mode, all the classifiers achieve a
perfect accuracy of 100%. In authentication mode, all three classifiers
achieved the highest accuracy (ACC) and Area Under Curve (AUC) from scenario B
using the point and click action data: (Decision Tree ACC:87.6%, AUC:90.3%),
(K-Nearest Neighbors ACC:99.3%, AUC:99.9%), and (Random Forest ACC:89.9%,
AUC:92.5%).
