Electronic Thesis and Dissertation Repository

Degree

Doctor of Philosophy

Program

Computer Science

Supervisor

Dr. Sylvia Osborn

Abstract

Incorporating risk consideration in access control systems has recently become a popular research topic. Related to this is risk awareness which is needed to enable access control in an agile and dynamic way. While risk awareness is probably known for an established access control system, being aware of risk even before the access control system is defined can mean identification of users and permissions that are most likely to lead to dangerous or error-prone situations from an administration point of view. Having this information available during the role engineering phase allows data analysts and role engineers to highlight potentially risky users and permissions likely to be misused. While there has been much recent work on role mining, there has been little consideration of risk during the process. In this thesis, we propose to add risk awareness to role mining. We aggregate the various possible risk factors and categorize them into four general types, which we refer to as risk metrics, in the context of role mining. Next, we propose a framework that incorporates some specific examples of each of these risk metrics before and after role mining. We have implemented a proof-of-concept prototype, a Risk Awareness system for Role Mining (aRARM) based on this framework and applied it to two case studies: a small organizational project and a university database setting. The aRARM prototype is automatically able to detect different types of risk factors when we add different types of noise to this data. The results from the two case studies draw some correlation between the behavior of the different risk factors due to different types and amounts of noise. We also discuss the effect of the different types and amounts of noise on the different role mining algorithms implemented for this study. While the detection rating value for calculating the risk priority number has previously been calculated after role mining, we attempt to find an initial estimate of the detection rating before role mining.


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