Intelligent online risk-based authentication using Bayesian network model

dc.contributor.authorLai, Dao Yu
dc.contributor.supervisorTraore, Issa
dc.date.accessioned2011-05-12T16:05:18Z
dc.date.available2011-05-12T16:05:18Z
dc.date.copyright2011en_US
dc.date.issued2011-05-12
dc.degree.departmentDepartment of Electrical and Computer Engineering
dc.degree.levelMaster of Applied Science M.A.Sc.en_US
dc.description.abstractRisk-based authentication is an increasingly popular component in the security architecture deployed by many organizations in mitigating online identity threat. Risk-based authentication uses contextual and historical information extracted from online communications to build a risk profile for the user that can be used to make accordingly authentication and authorization decisions. Existing risk-based authentication systems rely on basic web communication information such as the source IP address or the velocity of transactions performed by a specific account, or originating from a certain IP address. Such information can easily be spoofed and as such put in question the robustness and reliability of the proposed systems. In this thesis, we propose in this work an online risk-based authentication system which provides more robust user identity information by combining mouse dynamics, keystroke dynamics biometrics, and user site actions in a multimodal framework. We propose a Bayesian network model for analyzing free keystrokes and mouse movements involved in web sessions. Experimental evaluation of our proposed model with 24 participants yields an Equal Error Rate of 6.91%. This is encouraging considering that we are dealing with free text and mouse movements and the fact that many web sessions tend to be short.en_US
dc.description.scholarlevelGraduateen_US
dc.identifier.urihttp://hdl.handle.net/1828/3290
dc.languageEnglisheng
dc.language.isoenen_US
dc.rights.tempAvailable to the World Wide Weben_US
dc.subjectRisk-based authenticationen_US
dc.subjectnetwork securityen_US
dc.subjectmouse dynamicsen_US
dc.subjectkeystroke dynamicsen_US
dc.subjectbiometric technologyen_US
dc.subjectBayesian network modelen_US
dc.titleIntelligent online risk-based authentication using Bayesian network modelen_US
dc.typeThesisen_US

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