2026-09-20T14:39:31Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/117606 2023-07-11 col_2142_5131 col_2142_8888 com_2142_5130 com_2142_8887 com_2142_234

Hu, Yih-Chun 2022-12 application/pdf en text Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2024-12-01 The student, Yuhang Ren, accepted the attached license on 2022-12-08 at 10:33. The student, Yuhang Ren, submitted this Thesis for approval on 2022-12-08 at 10:36. This Thesis was approved for publication on 2022-12-08 at 15:54. DSpace SAF Submission Ingestion Package generated from Vireo submission #18771 on 2023-04-12 at 11:37:29 Multi-factor behavioral authentication based on webpage platform using combined data sources Ren, Yuhang 2022-12-08 Behavioral Biometrics Multi-factor Authentication Behavioral biometrics have been widely considered useful for user authentication. Behavioral biometrics include, but are not limited to, browser history, mouse dynamics, keystroke dynamics, GPS location, and so on. In this research, we focus on building a combined behavioral biometric authentication method based on a web platform where we collect users’ keystrokes, mouse movements, and interaction with the platform. For each of the data source, we build a neural network to authenticate the user. We then combine the networks together to reach a better accuracy of user authentication. The research aims to develop an authentication method that identify the user by studying behavioral data generated from webpage interactions, which would be difficult to hack and can be used while authentication methods such as passwords and facial recognition are vulnerable during attacks. Thesis eng https://hdl.handle.net/2142/117606 Copyright 2022 Yuhang Ren M.S. Thesis Electrical & Computer Engr University of Illinois at Urbana-Champaign Electrical & Computer Eng