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arXiv 2008.08444cs.CRcs.AI
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未知值下基于属性和基于知识的访问控制策略学习

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

Stony Brook University(石溪大学)

Thang Bui, Scott D. Stoller

摘要:

基于属性的访问控制(ABAC)和基于身份验证的访问控制(ReBAC)提供了高水平的表达能力和灵活性,通过允许策略以实体之间的属性和关系链的形式来表达,从而促进了安全性和信息共享。从传统访问控制信息中学习ABAC和ReBAC策略的算法有可能显著降低迁移到ABAC或ReBAC的成本。本文提出了第一个算法,挖掘ABAC和ReBAC策略从访问控制列表(ACL)和不完全信息的实体,其中一些属性的值是未知的。我们表明,这个问题的核心可以被看作是学习一个简洁的三值逻辑公式,从一组标记的特征向量包含未知数,我们给出了第一个算法(尽我们所知)的问题。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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