Continuous Authentication Using Mouse Clickstream Data Analysis
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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%).
KeywordsMouse dynamics Biometric Continuous authentication Behavioral biometric Machine learning
This research is partially supported by the Army Research Office (Contract No. W911NF-15-1-0524).
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