Risk-based access control systems built on fuzzy inferences

Abstract

Fuzzy inference is a promising approach to implement risk-based access control systems. However, its application to access control raises some novel problems that have not been yet investigated. First, because there are many different fuzzy operations, one must choose the fuzzy operations that best address security requirements. Second, risk-based access control, though it improves information flow and better addresses requirements from critical organizations, may result in damages by malicious users before mitigating steps are taken. Third, the scalability of a fuzzy inference-based access control system is questionable. The time required by a fuzzy inference engine to estimate risks may be quite high especially when there are tens of parameters and hundreds of fuzzy rules. However, an access control system may need to serve hundreds or thousands of users. In this paper, we investigate these issues and present our solutions or answers to them.

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