Towards Automated Learning of Access Control Policies Enforced by Web Applications
Towards Automated Learning of Access Control Policies Enforced by Web Applications
复制标题
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:
影响因子:
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通讯作者:
Masoumzadeh, Amir
中科院分区:
文献类型:
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作者:
Iyer, Padmavathi;Masoumzadeh, Amir
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.
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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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作者:
Bui, Thang;Stoller, Scott D.
通讯作者:
Stoller, Scott D.
DOI:
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发表时间:
2015
期刊:
ACM Symposium on Access Control Models and Technologies
影响因子:
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作者:
H. Le;Duy Cu Nguyen;L. Briand;Benjamin Hourte
通讯作者:
Benjamin Hourte
影响因子:
5.6
作者:
Bui, Thang;Stoller, Scott D.;Li, Jiajie
通讯作者:
Li, Jiajie
DOI:
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发表时间:
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