In the current intranet environment, information is becoming more readily accessed and replicated across a wide range of\ninterconnected systems. Anyone using the intranet computer may access content that he does not have permission to access. For an\ninsider attacker, it is relatively easy to steal a colleagueâ??s password or use an unattended computer to launch an attack. A common\none-time user authentication method may not work in this situation. In this paper, we propose a user authentication method\nbased on mouse biobehavioral characteristics and deep learning, which can accurately and efficiently perform continuous identity\nauthentication on current computer users, thus to address insider threats.We used an open-source dataset with ten users to carry\nout experiments, and the experimental results demonstrated the effectiveness of the approach. This approach can complete a user\nauthentication task approximately every 7 seconds, with a false acceptance rate of 2.94% and a false rejection rate of 2.28%.
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