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Code library for the Wendigo framework from 'Wendigo: Deep Reinforcement Learning for Denial-of-Service Query Discovery in GraphQL'

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Wendigo

Deep Reinforcement Learning for Denial-of-Service Query Discovery in GraphQL

This repository is the official release of the code used for the Wendigo Paper published in Deep Learning Security and Privacy Workshop (DSLP) 2024, co-located with IEEE S&P.

If you plan to use this repository in your projects, please cite the following paper:

@inproceedings{mcfadden2024wendigo,
  title = {Wendigo: Deep Reinforcement Learning for Denial-of-Service Query Discovery in GraphQL},
  author = {McFadden, Shae and Maugeri, Marcello and Hicks, Chris and Mavroudis, Vasilis and Pierazzi, Fabio},
  booktitle = {Proc. of the {IEEE} Workshop on Deep Learning Security and Privacy ({DLSP})},
  year = {2024},
}

Disclaimer

Please note that the code in this repository is only a research prototype and may generate damaging queries. Do not use against any real systems without prior, written consent of the targets. This code is released under a "Modified (Non-Commercial) BSD License": see the terms here.


Use Instructions

  1. python3 -m venv venv
  2. source venv/bin/activate
  3. pip3 install -r requirements.txt
  4. pip3 install .
  5. cd Wendigo
  6. python3 main.py

Wendigo Code Breakdown

The Wendigo code consists of the following sections:

  • attack: The code and results used to run the DoS impact evaluation which utilized the found query to perform a mock DoS attack.
  • environments: The environment is broken down into a parent and subclass.
    • GraphQLEnv.py: handles the docker and connection,
    • GraphQLDoSEnv.py: performs handles the mappings between agent's state/action and queries.
  • models: This directory contains the code for PPO and Random.
    • PPO.py: Contains the PPO code utilized for the evaluation of the paper and is a modified version of cleanrl's PPO implementation.
    • Random.py: Contains the implementation used for random evaluation of the paper (note: greedy is a setting in the attack settings).
  • results: This directory contains pickles of the results presented in the paper.
  • schemas: This contains the DVGA schema used to inform the environment for state to query space mappings.
  • settings: This directory contains the settings files used in the evaluation (Has its own read me for clarity on the settings).
  • utils: Contains helper functions and functions to interface between the agent and environment.
  • main.py: This file is used to run the application by specifying the variables below the application will load the appropriate setting file and run the experiment.
    • MODEL: 'PPO' or 'Random'
    • TEST: currently only 'DoS' is supported
    • DESC: 'Regular', 'Large', 'Greedy-Regular' or 'Greedy-Large'

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Code library for the Wendigo framework from 'Wendigo: Deep Reinforcement Learning for Denial-of-Service Query Discovery in GraphQL'

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