  An increasing body of work has recognized the importance of exploiting
machine learning (ML) advancements to address the need for efficient automation
in extracting access control attributes, policy mining, policy verification,
access decisions, etc. In this work, we survey and summarize various ML
approaches to solve different access control problems. We propose a novel
taxonomy of the ML model's application in the access control domain. We
highlight current limitations and open challenges such as lack of public
real-world datasets, administration of ML-based access control systems,
understanding a black-box ML model's decision, etc., and enumerate future
research directions.
