Continuous Authentication Using Mouse Clickstream Data Analysis
Sultan Almalki Prosenjit Chatterjee Kaushik Roy
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%).