Abstract
This study investigates the user identification method based on the computer mouse dynamic behaviors. One of the purposes is to refine seven types of mouse actions, and about 110 dimensional features have been employed from user session statistics and operating characteristics. And then, two basic techniques, principal component analysis (PCA) and weighted multiclassifier, are used to lay out the mouse behavior. Combing PCA and the new proposed classifier, two experiments on identification and authentication have been carried out. By validating the selected mouse data, the accuracy rate is as high as 85% in the identification experiment, and the false rejection rate (FRR) reaches 5.5% and false acceptance rate (FAR) reaches 8.8% in the authentication experiment. The results show that the proposed methods and the selected features can be well applied in practice.
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