Multi-factor behavioral authentication based on webpage platform using combined data sources
Ren, Yuhang
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Permalink
https://hdl.handle.net/2142/117606
Description
Title
Multi-factor behavioral authentication based on webpage platform using combined data sources
Author(s)
Ren, Yuhang
Issue Date
2022-12-08
Director of Research (if dissertation) or Advisor (if thesis)
Hu, Yih-Chun
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
behavioral biometrics
multi-factor authentication
Abstract
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.
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