# Machine Learning in Access Control: A Taxonomy and Survey

arXiv: https://arxiv.org/abs/2207.01739 · HTML: https://mlsanity.com/paper/2207.01739/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 140 references that are in the map.
- First posted on arXiv 4 Jul 2022.

## Builds on (most cited first, 26 of 26)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Towards Evaluating the Robustness of Neural Networks](https://mlsanity.com/paper/1608.04644/) (2016)
- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Adversarial Machine Learning at Scale](https://mlsanity.com/paper/1611.01236/) (2016)
- [Bidirectional LSTM-CRF Models for Sequence Tagging](https://mlsanity.com/paper/1508.01991/) (2015)
- [An Introduction to Convolutional Neural Networks](https://mlsanity.com/paper/1511.08458/) (2015)
- [Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Network](https://mlsanity.com/paper/1808.03314/) (2018)
- [Recurrent Neural Network Regularization](https://mlsanity.com/paper/1409.2329/) (2014)
- [Imperceptible Adversarial Attacks on Tabular Data](https://mlsanity.com/paper/1911.03274/) (2019)
- [Distributionally Adversarial Attack](https://mlsanity.com/paper/1808.05537/) (2018)
- [Learning Natural Language Inference using Bidirectional LSTM model and Inner-Attention](https://mlsanity.com/paper/1605.09090/) (2016)
- [Blocking Transferability of Adversarial Examples in Black-Box Learning Systems](https://mlsanity.com/paper/1703.04318/) (2017)
- [Mining Attribute-Based Access Control Policies from Logs](https://mlsanity.com/paper/1403.5715/) (2014)
- [Automated Verification of Neural Networks: Advances, Challenges and Perspectives](https://mlsanity.com/paper/1805.09938/) (2018)
- [Secure V2V and V2I Communication in Intelligent Transportation using Cloudlets](https://mlsanity.com/paper/2001.04041/) (2020)
- [Toward Deep Learning Based Access Control](https://mlsanity.com/paper/2203.15124/) (2022)
- [Efficient and Extensible Policy Mining for Relationship-Based Access Control](https://mlsanity.com/paper/1903.07530/) (2019)
- [An Automatic Attribute Based Access Control Policy Extraction from Access Logs](https://mlsanity.com/paper/2003.07270/) (2020)
- [Adaptive ABAC Policy Learning: A Reinforcement Learning Approach](https://mlsanity.com/paper/2105.08587/) (2021)
- [Greedy and Evolutionary Algorithms for Mining Relationship-Based Access Control Policies](https://mlsanity.com/paper/1708.04749/) (2017)
- [A Decision Tree Learning Approach for Mining Relationship-Based Access Control Policies](https://mlsanity.com/paper/1909.12095/) (2019)
- [Learning Attribute-Based and Relationship-Based Access Control Policies with Unknown Values](https://mlsanity.com/paper/2008.08444/) (2020)
- [PAMMELA: Policy Administration Methodology using Machine Learning](https://mlsanity.com/paper/2111.07060/) (2021)

## Built on by (most cited first, 3 of 3)

- [SoK: Access Control Policy Generation from High-level Natural Language Requirements](https://mlsanity.com/paper/2310.03292/) (2023)
- [Compiling Sufficient Governance Context from Declared Losses and Reachable States: Exact Observation-Contract Synthesis with Cardinality and Cost Objectives](https://mlsanity.com/paper/2609.26016/) (2026)
- [Towards Harnessing the Power of LLMs for ABAC Policy Mining](https://mlsanity.com/paper/2511.18098/) (2025)

## 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.