Machine Learning in Access Control: A Taxonomy and Survey
It provides a structured overview for researchers and practitioners in access control, but is incremental as a survey.
The paper surveys machine learning approaches for access control, proposing a taxonomy and identifying limitations like lack of public datasets and challenges in understanding black-box models.
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.