mlsanityData to 7 Oct 2026

Machine Learning in Access Control: A Taxonomy and Survey

arXiv 2207.01739 ↗First posted 4 Jul 2022cs.CR cs.LGcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 26 of its 140 references that are in the map. First posted on arXiv 4 Jul 2022.

Builds on

26 of 140 references.

References · 26

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14 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 0; 2025: 1; 2026: 1.

3 papers.

  1. 2023SoK: Access Control Policy Generation from High-level Natural Language Requirementscited by 5
  2. 2026Compiling Sufficient Governance Context from Declared Losses and Reachable States: Exact Observation-Contract Synthesis with Cardinality and Cost Objectivescited by 1
  3. 2025Towards Harnessing the Power of LLMs for ABAC Policy Miningcited by 0