基于正、负属性的访问控制策略规则挖掘

Padmavathi Iyer, A. Masoumzadeh
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引用次数: 35

摘要

挖掘访问控制策略可以根据系统中现有的授权信息自动生成策略,从而减轻采用更现代的访问控制模型的负担。这一领域以前的工作只侧重于挖掘积极的授权。这包括关于挖掘基于角色的访问控制策略(自然是关于积极授权的)的文献,以及最近关于挖掘基于属性的访问控制(ABAC)策略的工作。但是,各种理论访问控制模型(包括ABAC)、规范标准(如XACML)和实现(如操作系统和数据库)既支持负授权,也支持正授权。在本文中,我们提出了一种新的方法来挖掘可能包含正授权规则和负授权规则的ABAC策略。我们在正确性、规则质量(简洁性)和时间方面使用两种不同的策略来评估我们的方法。我们表明,在实现支持负面授权的新目标的同时,我们提出的算法在时间方面优于现有的ABAC挖掘方法。
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Mining Positive and Negative Attribute-Based Access Control Policy Rules
Mining access control policies can reduce the burden of adopting more modern access control models by automating the process of generating policies based on existing authorization information in a system. Previous work in this area has focused on mining positive authorizations only. That includes the literature on mining role-based access control policies (which are naturally about positive authorization) and even more recent work on mining attribute-based access control (ABAC) policies. However, various theoretical access control models (including ABAC), specification standards (such as XACML), and implementations (such as operating systems and databases) support negative authorization as well as positive authorization. In this paper, we propose a novel approach to mine ABAC policies that may contain both positive and negative authorization rules. We evaluate our approach using two different policies in terms of correctness, quality of rules (conciseness), and time. We show that while achieving the new goal of supporting negative authorizations, our proposed algorithm outperforms existing approach to ABAC mining in terms of time.
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