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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\u2019s 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%.<\/jats:p>","DOI":"10.1155\/2019\/3898951","type":"journal-article","created":{"date-parts":[[2019,2,17]],"date-time":"2019-02-17T18:31:29Z","timestamp":1550428289000},"page":"1-12","source":"Crossref","is-referenced-by-count":64,"title":["An Insider Threat Detection Approach Based on Mouse Dynamics and Deep Learning"],"prefix":"10.1155","volume":"2019","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8624-0210","authenticated-orcid":true,"given":"Teng","family":"Hu","sequence":"first","affiliation":[{"name":"Center for Cyber Security, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, 611731, China"},{"name":"Institute of Computer Application, China Academy of Engineering Physics, Mianyang, Sichuan, 621900, 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