Meta-RL for MTD

Code for our GameSec 2022 paper: Robust Moving Target Defense against Unknown Attacks: A Meta-Reinforcement Learning Approach

setup environment

Please run the following command to install required packages

# requirements
pip install -r requirements.txt

Usage

# Data Processing: Extract nvdcve-1.1-2021.json from /experiments/nvdcve-1.1-2021.zip and use NVD_parser to parse .json files
# Default settings: pre-train 10000 steps, adpate 100 steps, test 1000 steps. 
You can change these settings in main.py and select coresponding environment from experiments.py
You can also directly test/adapt ready-to-go policies stored in /experiments

Dataset:

We conducted numerical simulations using the real data from the National Vulnerability Database (NVD), which is stored at /experiments/nvdcve-1.1-2021.json. For more details, please visit: https://nvd.nist.gov/

Reproduce experiment results:

# Change the model dir to your own experiment
python3 main.py

Reference

NVD_parser is written by Tom Roginsky during our previous research on moving target defense supported by an NSF REU grant, the original code is avalible at https://github.com/MovingTargetDefenseCapstone/semester1

Citation

If you find our work useful in your research, please consider citing:

@inproceedings{li2023robust,
  title={Robust Moving Target Defense Against Unknown Attacks: A Meta-reinforcement Learning Approach},
  author={Li, Henger and Zheng, Zizhan},
  booktitle={Decision and Game Theory for Security: 13th International Conference, GameSec 2022, Pittsburgh, PA, USA, October 26--28, 2022, Proceedings},
  pages={107--126},
  year={2023},
  organization={Springer}
}