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一个加权的GraphSAGE基于大数据访问控制上下文意识的方法

Dibin Shan¹, Xuehui Du¹, Wenjuan Wang¹

¹Department of Information Systems Security, PLA Information Engineering University, Zhengzhou, China.

|August 1, 2023

概括

本研究引入了一种新的加权 GraphSAGE 方法来控制大数据的访问,从而实现自动上下文意识和关系推理. 该方法通过有效建模上下文关系来增强动态访问控制决策.

科学领域:

计算机科学计算机科学
数据科学数据科学数据科学
人工智能的人工智能

背景情况:

现有的上下文感知访问控制 (CAAC) 方法缺乏自动上下文感知和对上下文关系进行建模和推理的能力.
大数据的动态访问控制在很大程度上依赖于上下文信息,目前的方法无法自动充分利用这些信息.

研究的目的:

为大数据访问控制提出基于GraphSAGE的加权方法,该方法可实现自动上下文意识和推理.
解决现有的CAAC方法在自动建模和理解上下文关系方面的局限性.

主要方法:

访问记录数据的图形建模,将上下文意识转化为图形神经网络 (GNN) 节点学习问题.
开发一个GNN模型,WGraphSAGE,结合加权邻近采样和聚合算法.
通过GNN节点学习和关系强度建模,自动生成CAAC规则.

主要成果:

拟议的WGraphSAGE模型在上下文意识和关系推理方面表现优异,与类似的GNN模型相比.
实验结果显示,与现有的CAAC模型相比,动态访问控制决策具有显著的优势.
该方法有效地实现了节点关系及其优势的自动建模和推理.

结论:

权重的GraphSAGE方法为大数据访问控制中的自动上下文意识提供了一个强大的解决方案.
这种方法显著提高了动态访问控制决策的准确性和效率.
拟议的技术为理解和利用大数据环境中的复杂上下文关系提供了一个强大的工具.

关键词:

访问控制访问控制访问控制大数据就是大数据.环境意识背景意识图表神经网络的神经网络

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