{"title":"RanSAM: ABAC策略挖掘的随机搜索","authors":"Nakul Aggarwal, S. Sural","doi":"10.1145/3577923.3585050","DOIUrl":null,"url":null,"abstract":"This paper presents a novel approach for generating Attribute-based Access Control policies from a given Access Control Matrix (ACM). In contrast to the existing techniques for policy mining, which group the desired accesses in the ACM using certain heuristics, we pose it as a search problem in the policy space. A randomized algorithm is then used to identify the policy that best represents the given ACM. Our initial experiments show promising results.","PeriodicalId":387479,"journal":{"name":"Proceedings of the Thirteenth ACM Conference on Data and Application Security and Privacy","volume":"28 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"RanSAM: Randomized Search for ABAC Policy Mining\",\"authors\":\"Nakul Aggarwal, S. Sural\",\"doi\":\"10.1145/3577923.3585050\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a novel approach for generating Attribute-based Access Control policies from a given Access Control Matrix (ACM). In contrast to the existing techniques for policy mining, which group the desired accesses in the ACM using certain heuristics, we pose it as a search problem in the policy space. A randomized algorithm is then used to identify the policy that best represents the given ACM. Our initial experiments show promising results.\",\"PeriodicalId\":387479,\"journal\":{\"name\":\"Proceedings of the Thirteenth ACM Conference on Data and Application Security and Privacy\",\"volume\":\"28 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-04-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the Thirteenth ACM Conference on Data and Application Security and Privacy\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3577923.3585050\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Thirteenth ACM Conference on Data and Application Security and Privacy","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3577923.3585050","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
This paper presents a novel approach for generating Attribute-based Access Control policies from a given Access Control Matrix (ACM). In contrast to the existing techniques for policy mining, which group the desired accesses in the ACM using certain heuristics, we pose it as a search problem in the policy space. A randomized algorithm is then used to identify the policy that best represents the given ACM. Our initial experiments show promising results.
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