Learning Attribute-Based and Relationship-Based Access Control Policies with Unknown Values

08/19/2020 ∙ by Thang Bui, et al. ∙ 0 ∙ share

Attribute-Based Access Control (ABAC) and Relationship-based access control (ReBAC) provide a high level of expressiveness and flexibility that promote security and information sharing, by allowing policies to be expressed in terms of attributes of and chains of relationships between entities. Algorithms for learning ABAC and ReBAC policies from legacy access control information have the potential to significantly reduce the cost of migration to ABAC or ReBAC. This paper presents the first algorithms for mining ABAC and ReBAC policies from access control lists (ACLs) and incomplete information about entities, where the values of some attributes of some entities are unknown. We show that the core of this problem can be viewed as learning a concise three-valued logic formula from a set of labeled feature vectors containing unknowns, and we give the first algorithm (to the best of our knowledge) for that problem.

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Learning Attribute-Based and Relationship-Based Access Control Policies with Unknown Values

Authors

A Decision Tree Learning Approach for Mining Relationship-Based Access Control Policies

Efficient and Extensible Policy Mining for Relationship-Based Access Control

Graph Model Implementation of Attribute-Based Access Control Policies

Cascade Attribute Network: Decomposing Reinforcement Learning Control Policies using Hierarchical Neural Networks

A Precedent Approach to Assigning Access Rights

Practical Decentralized Attribute-Based Delegation using Secure Name Systems

Using the decision support algorithms combining different security policies

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