mlsanityData to 7 Oct 2026

Mining Attribute-Based Access Control Policies from Logs

arXiv 1403.5715 ↗First posted 23 Mar 2014cs.CR cs.DBcited by 10

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Abstract (arXiv)

Attribute-based access control (ABAC) provides a high level of flexibility that promotes security and information sharing. ABAC policy mining algorithms have potential to significantly reduce the cost of migration to ABAC, by partially automating the development of an ABAC policy from information about the existing access-control policy and attribute data. This paper presents an algorithm for mining ABAC policies from operation logs and attribute data. To the best of our knowledge, it is the first algorithm for this problem.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Mar 2014.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 2; 2023: 1; 2024: 0; 2025: 2; 2026: 3.

10 papers.

  1. 2025LMN: A Tool for Generating Machine Enforceable Policies from Natural Language Access Control Rules using LLMscited by 4
  2. 2022Toward Deep Learning Based Access Controlcited by 3
  3. 2022Machine Learning in Access Control: A Taxonomy and Surveycited by 3
  4. 2021Adaptive ABAC Policy Learning: A Reinforcement Learning Approachcited by 2
  5. 2021PAMMELA: Policy Administration Methodology using Machine Learningcited by 1
  6. 2026Compiling Sufficient Governance Context from Declared Losses and Reachable States: Exact Observation-Contract Synthesis with Cardinality and Cost Objectivescited by 1
  7. 2023Quantifying Policy Administration Cost in an Active Learning Frameworkcited by 0
  8. 2025Towards Harnessing the Power of LLMs for ABAC Policy Miningcited by 0
  9. 2026Generation of Human Comprehensible Access Control Policies from Audit Logscited by 0
  10. 2026Ajar: Measuring Open Privilege in Agent Defensescited by 0