Machine Learning in Access Control: A Taxonomy and Survey

• URL: http://arxiv.org/abs/2207.01739v1
• Date: Mon, 4 Jul 2022 22:36:27 GMT
• Title: Machine Learning in Access Control: A Taxonomy and Survey
• Authors: Mohammad Nur Nobi, Maanak Gupta, Lopamudra Praharaj, Mahmoud Abdelsalam, Ram Krishnan, Ravi Sandhu
• Abstract summary: We survey and summarize various machine learning approaches to solve different access control problems. 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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