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Identity fraud is spreading fast and causing more and more damages both financially and sociologically. Identity fraud occurs when a criminal impersonates another individual by taking on that person’s identity or by creating a fake identity for whatever reason. The project will investigate and develop a new model for identity fraud detection based on login sequence, history and context. Analysis of such information using data mining will allow tracking individual login behaviors and identifying anomalies and inconsistencies in login occurrences that potentially point to fraudulent activities. The outcome of the proposed research will be implemented as an add-on to the existing flagship authentication platform of the partner organization.
Mihai Sima
Amany Abdelhalim
Plurilock Security Solutions Inc.
Engineering - computer / electrical
Information and communications technologies
University of Victoria
Accelerate
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