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

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.

Automated summary

This paper proposes an algorithm for constructing an access control knowledge graph based on user and resource attributes, and introduces an online learning framework. The experimental results demonstrate that topological features extracted from the knowledge graph can enhance access control performance in scenarios with varying degrees of class imbalance.