Towards Automated Learning of Access Control Policies Enforced by Web Applications

Towards Automated Learning of Access Control Policies Enforced by Web Applications
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实现 Web 应用程序执行的访问控制策略的自动学习

DOI:
10.1145/3589608.3594743
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发表时间:
2023
期刊:
Proceedings of the 28th ACM Symposium on Access Control Models and Technologies
影响因子:
--
通讯作者:
Masoumzadeh, Amir
Masoumzadeh, Amir
中科院分区:
--
文献类型:
--
作者:
Iyer, Padmavathi;Masoumzadeh, Amir

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获得应用程序执行的访问控制策略的准确规范对于确保它满足我们的安全/隐私期望至关重要。这一点尤其重要,因为许多现实世界的应用程序处理大量和各种各样的数据对象,这些数据对象可能具有不同的适用策略。我们研究的问题,从Web应用程序的访问控制策略的自动学习。现有的访问控制策略挖掘的研究主要集中在开发算法,从低层次的授权信息推断出正确和简洁的策略。然而,在系统地收集低级别授权数据和应用程序的数据模型方面做得很少,这些数据是这种挖掘过程的先决条件。在本文中,我们提出了一种新的黑盒方法来推断这些先决条件,并讨论了我们的初步观察采用这样一个框架,从现实世界的Web应用程序的学习政策。
Obtaining an accurate specification of the access control policy enforced by an application is essential in ensuring that it meets our security/privacy expectations. This is especially important as many of real-world applications handle a large amount and variety of data objects that may have different applicable policies. We investigate the problem of automated learning of access control policies from web applications. The existing research on mining access control policies has mainly focused on developing algorithms for inferring correct and concise policies from low-level authorization information. However, little has been done in terms of systematically gathering the low-level authorization data and applications' data models that are prerequisite to such a mining process. In this paper, we propose a novel black-box approach to inferring those prerequisites and discuss our initial observations on employing such a framework in learning policies from real-world web applications.
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
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: --
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期刊: ACM Symposium on Access Control Models and Technologies
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DOI: 10.1016/j.cose.2018.09.011
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影响因子: 5.6
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