DADA: Dynamic authenticator for data access

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.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Electrical & Computer Engr

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2022

Author dc:creator

• Huang, Chenyang

Contributors dc:contributor

• Kalbarczyk, Zbigniew T

• Security
• Authentication
• Behavioral Biometrics Authentication
• Recurrent Neural Networks
• Electronic Medical Record (EMR)
• Data Engineering
• Machine Learning
• User Identification

Statement dc:rights

• © 2022 ChenYang Huang

Language dc:language

en, eng

Handle dc:identifier

https://hdl.handle.net/2142/116259

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F116259