{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/116259"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/116259","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"DADA: Dynamic authenticator for data access","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-15 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-15 without embargo terms","abstract_has_math":false,"creators":["Huang, Chenyang"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Kalbarczyk, Zbigniew T"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-08","date_published":"2022-08","updated_at":"2026-07-22T22:24:56Z","subjects":["Security","Authentication","Behavioral Biometrics Authentication","Recurrent Neural Networks","Electronic Medical Record (EMR)","Data Engineering","Machine Learning","User Identification"],"languages":["en","eng"],"rights":["© 2022 ChenYang Huang"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/116259","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Kalbarczyk, Zbigniew T"]},{"key":"dc:creator","label":"Author","values":["Huang, Chenyang"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-08","2022-07-20"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Security","Authentication","Behavioral Biometrics Authentication","Recurrent Neural Networks","Electronic Medical Record (EMR)","Data Engineering","Machine Learning","User Identification"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["© 2022 ChenYang Huang"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/116259"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-15 without embargo terms","The student, Chenyang Huang, accepted the attached license on 2022-07-15 at 23:32.","The student, Chenyang Huang, submitted this Thesis for approval on 2022-07-15 at 23:46.","This Thesis was approved for publication on 2022-07-20 at 15:54.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18353 on 2022-11-15 at 18:21:22","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."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["DADA: Dynamic authenticator for data access"]}]}],"canonical_facts":{"dc:contributor":["Kalbarczyk, Zbigniew T"],"dc:creator":["Huang, Chenyang"],"dc:date":["2022-08","2022-07-20"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-15 without embargo terms","The student, Chenyang Huang, accepted the attached license on 2022-07-15 at 23:32.","The student, Chenyang Huang, submitted this Thesis for approval on 2022-07-15 at 23:46.","This Thesis was approved for publication on 2022-07-20 at 15:54.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18353 on 2022-11-15 at 18:21:22","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."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/116259"],"dc:language":["en","eng"],"dc:rights":["© 2022 ChenYang Huang"],"dc:subject":["Security","Authentication","Behavioral Biometrics Authentication","Recurrent Neural Networks","Electronic Medical Record (EMR)","Data Engineering","Machine Learning","User Identification"],"dc:title":["DADA: Dynamic authenticator for data access"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Electrical & Computer Engr"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:56Z"}