Director of Research (if dissertation) or Advisor (if thesis)
Kalbarczyk, Zbigniew T
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)
Security
Authentication
Behavioral Biometrics Authentication
Recurrent Neural Networks
Electronic Medical Record (EMR)
Data Engineering
Machine Learning
User Identification
Language
eng
Abstract
Traditional authentication models for data access are vulnerable to prevalent attack vectors: insider attack, where one or more of the attackers is a genuine user that has proper access to the system; Hacking, where the attackers ac quire the credentials of a legitimate user in the system; Trojan attack, where the attackers injects malicious scripts on legitimate users’ computers. The Multi-Factor-Authentication scheme has gained much popularity in recent years. It remedies the shortcomings of password-based authentication, but is cumbersome and does not fully solve the problem with insider attack. In this study, we explore an approach to authenticate users based on what they do, rather than what they know. By monitoring users’ data access patterns, we show that it is possible to authenticate future access requests by checking if they conform to the established data access behavior pattern of the user.
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