Access control relationship prediction method based on GNN dual source learning

Dibin SHAN; Xuehui DU; Wenjuan WANG; Aodi LIU; Na WANG

doi:10.11959/j.issn.2096-109x.2022062

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Access control relationship prediction method based on GNN dual source learning

Topic: Big Data and Artifical Intelligence Security | 更新时间：2024-06-03

- Access control relationship prediction method based on GNN dual source learning
- Chinese Journal of Network and Information Security Vol. 8, Issue 5, Pages: 40-55(2022)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-109x.2022062
  CLC： TP309
- Online First：2022-10，
  
  Published：15 October 2022
- 稿件说明：
移动端阅览
Dibin SHAN, Xuehui DU, Wenjuan WANG, et al. Access control relationship prediction method based on GNN dual source learning[J]. Chinese Journal of Network and Information Security, 2022, 8(5): 40-55.
DOI：

Dibin SHAN, Xuehui DU, Wenjuan WANG, et al. Access control relationship prediction method based on GNN dual source learning[J]. Chinese Journal of Network and Information Security, 2022, 8(5): 40-55. DOI： 10.11959/j.issn.2096-109x.2022062.

摘要

随着大数据技术的迅速发展和广泛应用，用户越权访问成为制约大数据资源安全共享、受控访问的主要问题之一。基于关系的访问控制（ReBAC，relation-based access control）模型利用实体之间关系制定访问控制规则，增强了策略的逻辑表达能力，实现了动态访问控制，但仍然面临着实体关系数据缺失、规则的关系路径复杂等问题。为克服这些问题，提出了一种基于GNN双源学习的边预测模型——LPMDLG，将大数据实体关系预测问题转化为有向多重图的边预测问题。提出了基于有向包围子图的拓扑结构学习方法和有向双半径节点标记算法，通过有向包围子图提取、子图节点标记计算和拓扑结构特征学习3个环节，从实体关系图中学习节点与子图的拓扑结构特征；提出了基于有向邻居子图的节点嵌入特征学习方法，融入了注意力系数、关系类型等要素，通过有向邻居子图提取、节点嵌入特征学习等环节，学习其节点嵌入特征；设计了双源融合的评分网络，将拓扑结构与节点嵌入联合计算边的得分，从而获得实体关系图的边预测结果。边预测实验结果表明，相较于 R-GCN、SEAL、GraIL、TACT 等基线模型，所提模型在AUC-PR、MRR和Hits@N等评价指标下均获得更优的预测结果；消融实验结果说明所提模型的双源学习模式优于单一模式的边预测效果；规则匹配实验结果验证了所提模型实现了对部分实体的自动授权和对规则的关系路径的压缩。所提模型有效提升了边预测的效果，能够满足大数据访问控制关系预测需求。

Abstract

With the rapid development and wide application of big data technology

users’ unauthorized access to resources becomes one of the main problems that restrict the secure sharing and controlled access to big data resources.The ReBAC (Relationship-Based Access Control) model uses the relationship between entities to formulate access control rules

which enhances the logical expression of policies and realizes dynamic access control.However

It still faces the problems of missing entity relationship data and complex relationship paths of rules.To overcome these problems

a link prediction model LPMDLG based on GNN dual-source learning was proposed to transform the big data entity-relationship prediction problem into a link prediction problem with directed multiple graphs.A topology learning method based on directed enclosing subgraphs was designed in this modeled.And a directed dual-radius node labeling algorithm was proposed to learn the topological structure features of nodes and subgraphs from entity relationship graphs through three segments

including directed enclosing subgraph extraction

subgraph node labeling calculation and topological structure feature learning.A node embedding feature learning method based on directed neighbor subgraph was proposed

which incorporated elements such as attention coefficients and relationship types

and learned its node embedding features through the sessions of directed neighbor subgraph extraction and node embedding feature learning.A two-source fusion scoring network was designed to jointly calculate the edge scores by topology and node embedding to obtain the link prediction results of entity-relationship graphs.The experiment results of link prediction show that the proposed model obtains better prediction results under the evaluation metrics of AUC-PR

MRR and Hits@N compared with the baseline models such as R-GCN

SEAL

GraIL and TACT.The ablation experiment results illustrate that the model’s dual-source learning scheme outperforms the link prediction effect of a single scheme.The rule matching experiment results verify that the model achieves automatic authorization of some entities and compression of the relational path of rules.The model effectively improves the effect of link prediction and it can meet the demand of big data access control relationship prediction.

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