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Increasing Efficiency and Scalability in AWS IAM by Leveraging an Entity-centric Attribute- & Role-based Access Control (EARBAC) ModelKarlsson, Rasmus, Jönrup, Pontus January 2023 (has links)
Cloud computing is becoming increasingly popular among all types of companies due to its inherent benefits. However, because of its infrastructure, it might be difficult to manage access rights between users and resources. To address these difficulties, Amazon Web Services (AWS) provides Identity and Access Management (IAM) and features that support the use of different access control models, for example, Role-based Access Control (RBAC) and Attribute-based Access Control (ABAC). Access control models are used for authorisation within systems to decide who gets access to what. Therefore, to determine what constitutes an efficient (the average time it takes to perform a task in AWS IAM) and secure access control model, a thorough study of background material and related work was conducted. Through this study, it was found that RBAC lacked scalability whilst ABAC lacked administrative capabilities. It was also found that flexibility and scalability were two important factors when designing access control models. Furthermore, by conducting a survey and designing an access control model for AWS through various iterations, a new access control model called Entity-centric Attribute- & Role-based Access Control (EARBAC) was developed. In an experiment comparing it with the RBAC model, the EARBAC model was found to be both efficient and secure, in addition to its flexibility and scalability. Furthermore, EARBAC was also found to be 27% faster than RBAC in AWS IAM. These results suggest that the model is useful when developing cloud infrastructures in AWS.
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