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Continuous Authentication Using Mouse Clickstream Data Analysis

  • Sultan AlmalkiEmail author
  • Prosenjit ChatterjeeEmail author
  • Kaushik RoyEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11637)

Abstract

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%).

Keywords

Mouse dynamics Biometric Continuous authentication Behavioral biometric Machine learning 

Notes

Acknowledgment

This research is partially supported by the Army Research Office (Contract No. W911NF-15-1-0524).

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer ScienceNorth Carolina A&T State UniversityGreensboroUSA

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