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arXiv 2402.08299cs.CR
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企业网络中基于零信任评分的网络级访问控制

Zero Trust Score-based Network-level Access Control in Enterprise Networks

Leonard Bradatsch, Oleksandr Miroshkin, Natasa Trkulja, Frank Kargl

AI总结:

本文针对企业网络零信任安全,提出29个信任属性量化方法、动态风险信任阈值计算及基于Subjective Logic的细粒度信任算法,在保持与加法算法相似性能的同时提升了表达能力。

摘要:

零信任安全近期在企业网络安全中受到关注。其关键理念之一是基于信任评分做出网络级访问决策。然而,企业领域中基于评分的访问控制在理解上仍缺乏基本要素,本文在三个关键方面做出了贡献。首先,我们提供了可用于计算信任评分的29个信任属性的全面列表。通过引入一种新颖的数学方法,我们演示了如何量化这些属性。其次,我们描述了一种动态的基于风险的方法,用于计算允许访问所需的信任阈值。第三,我们引入了一种基于Subjective Logic(主观逻辑)的新颖信任算法,该算法结合了前两项贡献,并提供了细粒度的决策可能性。我们讨论了与轻量级加法信任算法相比,该算法如何展现出更高的表达能力。在性能方面,基于Subjective Logic方法的原型在做出访问决策时显示出与加法方法相似的计算时间。此外,动态阈值计算显示,与静态阈值相比,决策时间仅增加7%。

英文摘要:

Zero Trust security has recently gained attention in enterprise network security. One of its key ideas is making network-level access decisions based on trust scores. However, score-based access control in the enterprise domain still lacks essential elements in our understanding, and in this paper, we contribute with respect to three crucial aspects. First, we provide a comprehensive list of 29 trust attributes that can be used to calculate a trust score. By introducing a novel mathematical approach, we demonstrate how to quantify these attributes. Second, we describe a dynamic risk-based method to calculate the trust threshold the trust score must meet for permitted access. Third, we introduce a novel trust algorithm based on Subjective Logic that incorporates the first two contributions and offers fine-grained decision possibilities. We discuss how this algorithm shows a higher expressiveness compared to a lightweight additive trust algorithm. Performance-wise, a prototype of the Subjective Logic-based approach showed similar calculation times for making an access decision as the additive approach. In addition, the dynamic threshold calculation showed only 7% increased decision-making times compared to a static threshold.

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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