Time changing decision tree is a learning algorithm that can infer policies from training data with policy changes. Examples of such policies and data includes access control policies and access logs. For example, along the time, a website admin may make some website resource, like url /exam/midterm.pdf, public or private, the corresponding access logs could be
GET /exam/midterm.pdf 200
GET /exam/midterm.pdf 403
This makes previous entropy or gini-based decision tree learning algorithms less effective on inferring the change as a single valid policy. For more details, please read our paper Towards Continuous Access Control Validation and Forensics.
python setup.py install --user
Please look into example.ipynb or example.py for how to use tcdt.
If you would like to use this tool, please cite our paper.
@inproceedings{xiang2019towards,
title={Towards Continuous Access Control Validation and Forensics},
author={Xiang, Chengcheng and Wu, Yudong and Shen, Bingyu and Shen, Mingyao and Huang, Haochen and Xu, Tianyin and Zhou, Yuanyuan and Moore, Cindy and Jin, Xinxin and Sheng, Tianwei},
booktitle={Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security},
pages={113--129},
year={2019},
organization={ACM}
}