Administration of Machine Learning Based Access Control

Author:

Nobi Mohammad Nur,Krishnan Ram,Huang Yufei,Sandhu Ravi

Publisher

Springer Nature Switzerland

Reference41 articles.

1. Lecture Notes in Computer Science;A Abu Jabal,2020

2. Alkhresheh, A., Elgazzar, K., Hassanein, H.S.: Adaptive access control policies for IoT deployments. In: IEEE IWCMC (2020)

3. Alohaly, M., Takabi, H., Blanco, E.: A deep learning approach for extracting attributes of ABAC policies. In: ACM SACMAT (2018)

4. Amazon, K.: Amazon employee access challenge in Kaggle (2013). https://www.kaggle.com/c/amazon-employee-access-challenge/

5. Amazon, U.: Amazon access samples data set (2011). http://archive.ics.uci.edu/ml/datasets/Amazon+Access+Samples

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1. Attacks and Defences for ML-enhanced Access Control;Proceedings of the 24th International Middleware Conference: Demos, Posters and Doctoral Symposium;2023-12-11

2. Zero Trust Context-Aware Access Control Framework for IoT Devices in Healthcare Cloud AI Ecosystem;2023-09-15

3. Effective Machine Learning-based Access Control Administration through Unlearning;2023 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW);2023-07

4. Poster: How to Raise a Robot - Beyond Access Control Constraints in Assistive Humanoid Robots;Proceedings of the 28th ACM Symposium on Access Control Models and Technologies;2023-05-24

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