Efficient Access Control Permission Decision Engine Based on Machine Learning
Efficient Access Control Permission Decision Engine Based on Machine Learning
复制标题
基于机器学习的高效访问控制权限决策引擎
DOI：
10.1155/2021/3970485
复制
发表时间：
2021-02
影响因子：
--
通讯作者：
Wang Na
中科院分区：
文献类型：
--
作者：
Liu Aodi;Du Xuehui;Wang Na
访问控制技术对信息系统的安全可靠运行至关重要。然而，在大数据、物联网、云计算等开放分布式信息系统中，由于访问控制实体的数量和策略规模巨大，现有的访问控制权限决策方法存在性能瓶颈。因此，大量的访问控制时间开销影响了业务服务的正常运行。为了克服上述问题，本文提出了一种高效的基于机器学习的权限决策引擎方案（EPDE-ML）。该方案将基于属性的访问控制请求转化为一个权限决策向量，将访问控制权限决策问题转化为允许或拒绝访问的二元分类问题.采用随机森林算法构造向量决策分类器，建立高效的权限决策引擎。实验结果表明，该方法在测试数据集上的权限决策准确率达到92.6%，且其权限决策效率明显高于基准方法.此外，随着政策规模的增加，其绩效改善也更加明显。
Access control technology is critical to the safe and reliable operation of information systems. However, owing to the massive policy scale and number of access control entities in open distributed information systems, such as big data, the Internet of Things, and cloud computing, existing access control permission decision methods suffer from a performance bottleneck. Consequently, the large access control time overhead affects the normal operation of business services. To overcome the above-mentioned problem, this paper proposes an efficient permission decision engine scheme based on machine learning (EPDE-ML). The proposed scheme converts the attribute-based access control request into a permission decision vector, and the access control permission decision problem is transformed into a binary classification problem that allows or denies access. The random forest algorithm is used to construct a vector decision classifier in order to establish an efficient permission decision engine. Experimental results show that the proposed method can achieve a permission decision accuracy of around 92.6% on a test dataset, and its permission decision efficiency is significantly higher than that of the benchmark method. In addition, its performance improvement becomes more obvious as the scale of policy increases.
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DOI：
10.1145/2188286.2188346
发表时间：
2012-04
期刊：
--
影响因子：
--
作者：
Donia El Kateb;Tejeddine Mouelhi;Y. L. Traon;JeeHyun Hwang;Tao Xie
通讯作者：
Donia El Kateb;Tejeddine Mouelhi;Y. L. Traon;JeeHyun Hwang;Tao Xie
DOI：
--
发表时间：
2018
期刊：
--
影响因子：
--
作者：
通讯作者：
--
DOI：
10.1109/pst.2012.6297915
发表时间：
2012-07
期刊：
2012 Tenth Annual International Conference on Privacy, Security and Trust
影响因子：
--
作者：
Marian Harbach;S. Fahl;Michael Brenner;T. Muders;Matthew Smith
通讯作者：
Marian Harbach;S. Fahl;Michael Brenner;T. Muders;Matthew Smith
影响因子：
3.7
作者：
Liu, Alex X.;Chen, Fei;Xie, Tao
通讯作者：
Xie, Tao
DOI：
10.1145/2295136.2295153
发表时间：
2012-06
期刊：
--
影响因子：
--
作者：
Santiago Pina Ros;Mario Lischka;Félix Gómez Mármol
通讯作者：
Santiago Pina Ros;Mario Lischka;Félix Gómez Mármol