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

A Decision Tree Learning Approach for Mining Relationship-Based Access Control Policies
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挖掘基于关系的访问控制策略的决策树学习方法

DOI:
10.1145/3381991.3395619
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发表时间:
2020
期刊:
Proceedings of the 25th ACM Symposium on Access Control Models and Technologies (SACMAT 2020
影响因子:
--
通讯作者:
Stoller, Scott D.
Stoller, Scott D.
中科院分区:
--
文献类型:
--
作者:
Bui, Thang;Stoller, Scott D.

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基于关系的访问控制(ReBAC)提供了高度的表现力和灵活性,通过允许用实体之间的关系链来表达策略,从而促进了安全和信息共享。通过部分自动化ReBAC策略的开发,ReBAC策略挖掘算法有可能显著降低从传统访问控制系统迁移到ReBAC的成本。提出了一种基于决策树的ReBAC策略挖掘算法DTRM(Decision Tree ReBAC Miner)和DTRM-,用于从访问控制列表(ACL)和实体信息中挖掘ReBAC策略。与最先进的ReBAC挖掘算法相比,我们的算法明显更快,获得了类似的策略质量,并且可以用更丰富的语言挖掘策略。
Relationship-based access control (ReBAC) provides a high level of expressiveness and flexibility that promotes security and information sharing, by allowing policies to be expressed in terms of chains of relationships between entities. ReBAC policy mining algorithms have the potential to significantly reduce the cost of migration from legacy access control systems to ReBAC, by partially automating the development of a ReBAC policy. This paper presents new algorithms, called DTRM (Decision Tree ReBAC Miner) and DTRM-, based on decision trees, for mining ReBAC policies from access control lists (ACLs) and information about entities. Compared to state-of-the-art ReBAC mining algorithms, our algorithms are significantly faster, achieve comparable policy quality, and can mine policies in a richer language.
从不完整和噪声数据中挖掘基于关系的访问控制策略
DOI: 10.1007/978-3-030-18419-3_18
发表时间: 2018
期刊: Proceedings of the Eleventh ACM Conference on Data and Application Security and Privacy
影响因子: --
作者:
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DOI: --
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影响因子: --
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