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Issue No. 03 - July-September (2007 vol. 4)
ISSN: 1545-5971
pp: 165-179
In this paper we introduce a new form of behavioral biometrics based on mouse dynamics, which can be used indifferent security applications. We develop a technique that can be used to model the behavioral characteristics from the captured data using artificial neural networks. In addition, we present an architecture and implementation for the detector, which cover all the phases of the biometric data flow including the detection process. Experimental data illustrating the experiments conducted to evaluate the accuracy of the proposed detection technique are presented and analyzed. Specifically, three series of experiments are conducted. The main experiment, in which twenty-two participants are involved, reproduces real operating conditions in computing systems by giving participants an individual choice of operating environments and applications; 284 hours of raw mouse data are collected over 998 sessions, with an average of 45 sessions per user. The two other experiments, involving seven participants, provided a basis for studying the confounding factors arising from the main experiment by fixing the environment variables. In the main experiment, the performance results presented using receiver operating characteristic(ROC) curves and a confusion matrix yield at the crossover point (i.e., threshold set for an equal error rate) a false acceptance rate (FAR) of 2.4649% and a false rejection rate (FRR) of 2.4614%.
Biometrics, Mouse Dynamics, Security Monitoring, Network Security, Human Computer Interaction.

A. A. Ahmed and I. Traore, "A New Biometric Technology Based on Mouse Dynamics," in IEEE Transactions on Dependable and Secure Computing, vol. 4, no. , pp. 165-179, 2007.
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