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The rapid proliferation of computing processing power has facilitated a rise in the adoption of computers in various aspects of human lives. From education to shopping and other everyday activities to critical applications in finance, banking and, recently, degree awarding online education. Several approaches for user authentication based on Behavioral Biometrics (BB) were suggested in order to identify unique signature/footprint for improved matching accuracy for genuine users and flagging for abnormal behaviors from intruders. In this paper we present a comparison between two classification algorithms for identifying users' behavior using mouse dynamics. The algorithms are based on support vector machines (SVM) classifier allowing for direct comparison between different authentication-based metrics. The voting technique shows low False Acceptance Rate(F AR) and noticeably small learning time; making it more suitable for incorporation within different authentication applications.

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