基于GNN双源学习的访问控制关系预测方法

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

Abstract

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.

Key words: big data, relationship-based access control, link predication, graph neural network

CLC Number:

TP309

Dibin SHAN, Xuehui DU, Wenjuan WANG, Aodi LIU, Na WANG. Access control relationship prediction method based on GNN dual source learning[J]. Chinese Journal of Network and Information Security, 2022, 8(5): 40-55.

Figures/Tables 12

数据集	版本	R-GCN*	SEAL*	GraIL**	TACT**	LPMDLG
WN18RR	v2	66.12	77.91	94.18	$97 . 95$	97.17
	v3	60.7	76.34	85.8	90.58	$92 . 02$
FB15k-237	v2	69.74	70.59	90.57	94.20	$95 . 97$
	v3	63.24	69.52	91.68	97.10	$97 . 83$
NELL-995	v2	67.55	71.03	92.62	96.58	$96 . 63$
	v3	68.87	68.92	93.34	95.70	$96 . 22$

数据集	版本	R-GCN*	SEAL*	GraIL**	TACT**	LPMDLG
WN18RR	V2	0.637	0.621	0.772	0.984	$0 . 988$
	V3	0.489	0.511	0.549	0.895	$0 . 922$
FB15k-237	V2	0.051	0.062	0.049	0.815	$0 . 902$
	V3	0.066	0.040	0.045	0.752	$0 . 883$
NELL-995	V2	0.071	0.055	0.058	0.680	$0 . 702$
	V3	0.062	0.049	0.042	0.548	$0 . 632$

数据集	版本	R-GCN*	SEAL*	GraIL**	TACT**	LPMDLG
WN18RR	V2	0.611	0.595	0.628	0.978	$0 . 976$
	V3	0.584	0.411	0.377	0.852	$0 . 902$
FB15k-237	V2	0.376	0.327	0.013	0.717	$0 . 834$
	V3	0.491	0.233	0.003	0.609	$0 . 801$
NELL-995	V2	0.51	0.49	0.01	0.533	$0 . 634$
	V3	0.433	0.462	0.007	0.354	$0 . 601$

数据集	版本	G		V		LPMDLG
数据集	版本	MRR	Hits@1	MRR	Hits@1	MRR	Hits@1
WN18RR	V2	0.857	0.742	0.841	0.897	$0 . 988$	$0 . 976$
	V3	0.902	0.891	0.913	0.812	$0 . 922$	$0 . 902$
FB15k-237	V2	0.836	0.763	0.811	0.82	$0 . 902$	$0 . 834$
	V3	0.832	0.760	0.715	0.698	$0 . 883$	$0 . 801$
NELL-995	V2	0.627	0.590	0.679	0.623	$0 . 702$	$0 . 632$
	V3	0.604	0.527	0.575	0.515	$0 . 634$	$0 . 601$

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数据集	实体数量	SRA数量	实体关系数量和		SRA=T数量		规则匹配长度和		边增长	SRA=T增长	匹配开销减少
数据集	实体数量	SRA数量	预测前	预测后	预测前	预测后	预测前	预测后	边增长	SRA=T增长	匹配开销减少
EMR_15	353	4 134	877	937	1 020	1 106	22 091	20 195	6.842%	8.431%	8.583%
healthcare_5	736	42 121	1 804	1 921	5 931	6 168	25 783	24 927	6.486%	3.996%	3.320%
Project-mgmt_5	179	4 080	296	325	981	1 002	19 829	19 035	9.797%	2.141%	4.004%
University_5	738	83 761	926	1 107	25 018	26 175	46 926	45 820	19.546%	4.625%	2.357%
e-document_175	563	152 093	2 830	3 256	40 937	42 192	682 960	670 291	15.053%	3.066%	1.855%
eWorkforce_30	1 016	104 845	2 928	3 471	35 206	36 332	539 172	512 955	18.545%	3.198%	4.862%

Access control relationship prediction method based on GNN dual source learning

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