A knowledge graph empowered online learning framework for access control decision-making

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You, Mingshan ORCID: 0000-0003-0958-528X, Yin, Jiao ORCID: 0000-0002-0269-2624, Wang, Hua ORCID: 0000-0002-8465-0996, Cao, Jinli ORCID: 0000-0002-0221-6361, Wang, Kate, Miao, Yuan ORCID: 0000-0002-6712-3465 and Bertino, E (2022) A knowledge graph empowered online learning framework for access control decision-making. World Wide Web. ISSN 1386-145X

Abstract

Knowledge graph, as an extension of graph data structure, is being used in a wide range of areas as it can store interrelated data and reveal interlinked relationships between different objects within a large system. This paper proposes an algorithm to construct an access control knowledge graph from user and resource attributes. Furthermore, an online learning framework for access control decision-making is proposed based on the constructed knowledge graph. Within the framework, we extract topological features to represent high cardinality categorical user and resource attributes. Experimental results show that topological features extracted from knowledge graph can improve the access control performance in both offline learning and online learning scenarios with different degrees of class imbalance status.

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Item type Article
URI https://vuir.vu.edu.au/id/eprint/44500
DOI https://doi.org/10.1007/s11280-022-01076-5
Official URL https://link.springer.com/article/10.1007/s11280-0...
Subjects Current > FOR (2020) Classification > 4602 Artificial intelligence
Current > FOR (2020) Classification > 4605 Data management and data science
Current > Division/Research > College of Science and Engineering
Current > Division/Research > Institute for Sustainable Industries and Liveable Cities
Keywords knowledge graph, graph data structure, access control, security
Citations in Scopus 2 - View on Scopus
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