基于权限聚类的属性值优化

毋文超; 任志宇; 杜学绘

doi:10.11959/j.issn.2096-109x.2021077

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基于权限聚类的属性值优化

学术论文 | 更新时间：2024-06-03

- 基于权限聚类的属性值优化
- Permission clustering-based attribute value optimization
- 网络与信息安全学报 2021年7卷第4期页码：175-182
- 作者机构：
  
  1. 信息工程大学，河南郑州 450001
  2. 中国人民解放军31668部队，青海西宁 810000
- 作者简介：
  
  [ "毋文超（1995− ），男，河南焦作人，中国人民解放军 31668 部队助理工程师，主要研究方向为授权管理与访问控制" ]
  [ "任志宇（1974− ），女，河南汤阴人，博士，信息工程大学副教授，主要研究方向为网络与信息安全" ]
  [ "杜学绘（1968− ），女，河南新乡人，博士，信息工程大学教授、博士生导师，主要研究方向为网络与信息安全、空间信息网络、云计算安全" ]
- 基金信息：
  
  国家重点研发计划;The National Key R＆D Program of China(2018YFB0803603);国家自然科学基金;The National Natural Science Foundation of China(61702550;61802436)
- DOI：10.11959/j.issn.2096-109x.2021077
  中图分类号： TP393
- 网络首发：2021-08，
  
  纸质出版：2021-08-15
- 稿件说明：
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毋文超, 任志宇, 杜学绘. 基于权限聚类的属性值优化[J]. 网络与信息安全学报, 2021,7(4):175-182.

Wenchao WU, Zhiyu REN, Xuehui DU. Permission clustering-based attribute value optimization[J]. Chinese Journal of Network and Information Security, 2021, 7(4): 175-182.
毋文超, 任志宇, 杜学绘. 基于权限聚类的属性值优化[J]. 网络与信息安全学报, 2021,7(4):175-182. DOI： 10.11959/j.issn.2096-109x.2021077.

Wenchao WU, Zhiyu REN, Xuehui DU. Permission clustering-based attribute value optimization[J]. Chinese Journal of Network and Information Security, 2021, 7(4): 175-182. DOI： 10.11959/j.issn.2096-109x.2021077.

摘要

在新型大规模计算环境下应用ABAC（基于属性的访问控制）面临着属性数量多、来源复杂、质量参差不齐、难以人工修正、难以直接应用于访问控制的问题。针对属性标称值的优化问题，设计了一种基于权限聚类的属性值优化算法，通过将实体表示成对应的权限集合，对实体进行基于密度的聚类，为实体赋予权限对应的类别标签，而后基于粗糙集理论对属性值进行化简与修正。最后在 UCI 公开数据集上对算法进行了验证，证明应用该算法后，ABAC策略挖掘在真阳性率和F1得分上均具有较大的提升。

Abstract

In new large-scale computing environment

the attributes of entities were massive and they had complex sources and uneven quality

which were great obstacles to the application of ABAC (attribute-based access control).The attributes were also hard to be corrected manually

making it difficult to be applied in access control system straightly.To solve the optimization problem of nominal attributes

a novel algorithm of attribute value optimization based on permission clustering was designed

in which entities were presented by the privilege set related to them.So that the entities were tagged by density-based clustering method with distances of their privilege set presentations.Then the attribute values were reduced and corrected based on rough set theory.Finally

the algorithm was verified on UCI data sets

which proved that after applying it

ABAC policy mining was improved in the evaluation criteria

such as the true positive rate and F1-score.

关键词

Keywords

references

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