An Insider Threat Detection Approach Based on Mouse Dynamics and Deep Learning

• Main Authors: Teng Hu, Weina Niu, Xiaosong Zhang, Xiaolei Liu, Jiazhong Lu, Yuan Liu
• Format: Article
• Language: English
• Published: Hindawi-Wiley 2019-01-01
• Series: Security and Communication Networks
• Online Access: http://dx.doi.org/10.1155/2019/3898951

In the current intranet environment, information is becoming more readily accessed and replicated across a wide range of interconnected systems. Anyone using the intranet computer may access content that he does not have permission to access. For an insider attacker, it is relatively easy to steal a colleague’s password or use an unattended computer to launch an attack. A common one-time user authentication method may not work in this situation. In this paper, we propose a user authentication method based on mouse biobehavioral characteristics and deep learning, which can accurately and efficiently perform continuous identity authentication on current computer users, thus to address insider threats. We used an open-source dataset with ten users to carry out experiments, and the experimental results demonstrated the effectiveness of the approach. This approach can complete a user authentication task approximately every 7 seconds, with a false acceptance rate of 2.94% and a false rejection rate of 2.28%.