{"title":"基于监督学习的ABAC规则挖掘方法","authors":"G. Sahani, Chirag S. Thaker, Sanjay M. Shah","doi":"10.4108/eetsis.v5i16.1560","DOIUrl":null,"url":null,"abstract":"Attribute-Based Access Control (ABAC) is an emerging access control model. It is the more flexible, scalable, and most suitable access control model for today’s large-scale, distributed, and open application environments. It has become an emerging research area nowadays. However, Role-Based Access Control (RBAC) has been the most widely used and general access control model so far. It is simple in administration and policy definition. But user-to-role assignment process of RBAC makes it non-scalable for large-scale organizations with a large number of users. To scale up the growing organization, RBAC needs to be transformed into ABAC. Transforming existing RBAC systems into ABAC is complicated and time-consuming. In this paper, we present a supervised machine learning-based approach to extract attribute-based conditions from the existing RBAC system to construct ABAC rules at the primary level and simplify the process of the transforming RBAC system to ABAC.","PeriodicalId":43034,"journal":{"name":"EAI Endorsed Transactions on Scalable Information Systems","volume":"10 1","pages":"e9"},"PeriodicalIF":1.1000,"publicationDate":"2022-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Supervised Learning-Based Approach Mining ABAC Rules from Existing RBAC Enabled Systems\",\"authors\":\"G. Sahani, Chirag S. Thaker, Sanjay M. Shah\",\"doi\":\"10.4108/eetsis.v5i16.1560\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Attribute-Based Access Control (ABAC) is an emerging access control model. It is the more flexible, scalable, and most suitable access control model for today’s large-scale, distributed, and open application environments. It has become an emerging research area nowadays. However, Role-Based Access Control (RBAC) has been the most widely used and general access control model so far. It is simple in administration and policy definition. But user-to-role assignment process of RBAC makes it non-scalable for large-scale organizations with a large number of users. To scale up the growing organization, RBAC needs to be transformed into ABAC. Transforming existing RBAC systems into ABAC is complicated and time-consuming. In this paper, we present a supervised machine learning-based approach to extract attribute-based conditions from the existing RBAC system to construct ABAC rules at the primary level and simplify the process of the transforming RBAC system to ABAC.\",\"PeriodicalId\":43034,\"journal\":{\"name\":\"EAI Endorsed Transactions on Scalable Information Systems\",\"volume\":\"10 1\",\"pages\":\"e9\"},\"PeriodicalIF\":1.1000,\"publicationDate\":\"2022-09-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"EAI Endorsed Transactions on Scalable Information Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4108/eetsis.v5i16.1560\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"EAI Endorsed Transactions on Scalable Information Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4108/eetsis.v5i16.1560","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
Supervised Learning-Based Approach Mining ABAC Rules from Existing RBAC Enabled Systems
Attribute-Based Access Control (ABAC) is an emerging access control model. It is the more flexible, scalable, and most suitable access control model for today’s large-scale, distributed, and open application environments. It has become an emerging research area nowadays. However, Role-Based Access Control (RBAC) has been the most widely used and general access control model so far. It is simple in administration and policy definition. But user-to-role assignment process of RBAC makes it non-scalable for large-scale organizations with a large number of users. To scale up the growing organization, RBAC needs to be transformed into ABAC. Transforming existing RBAC systems into ABAC is complicated and time-consuming. In this paper, we present a supervised machine learning-based approach to extract attribute-based conditions from the existing RBAC system to construct ABAC rules at the primary level and simplify the process of the transforming RBAC system to ABAC.