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YAWNING-TITAN

About The Project

YAWNING-TITAN (YT) is an abstract, graph based cyber-security simulation environment that supports the training of intelligent agents for autonomous cyber operations. YAWNING-TITAN currently only supports defensive autonomous agents who face off against probabilistic red agents.

YT has been designed with the following things in mind:

• Simplicity over complexity
• Minimal Hardware Requirements
• Operating System agnostic
• Support for a wide range of algorithms
• Enhanced agent/policy evaluation support
• Flexible environment and game rule configuration
• Generation of evaluation episode visualisations (gifs)

YT was publicly released on 20th July 2022 under MIT licence. It will continue to be developed through the Autonomous Resilient Cyber Defence (ARCD) project, overseen by Dstl.

Contributing to YAWNING-TITAN

Found a bug, have an idea/feature you'd like to suggest, or just want to get involved with the YT community, please read our How to contribute to YAWNING-TITAN? guidelines.

What's YAWNING-TITAN built with

• OpenAI's Gym
• Networkx
• Stable Baselines 3
• Rllib (part of Ray)

Getting Started with YAWNING-TITAN

Pre-Requisites

In order to get YT installed, you will need to have the following installed:

• python3.8+
• python3-pip
• virtualenv

YT is designed to be OS-agnostic, and thus should work on most variations/distros of Linux, Windows, and MacOS.

Installation from source

1. Navigate to the YAWNING-TITAN folder and create a new python virtual environment (venv)

python3 -m venv <name_of_venv>

2. Activate the venv

Unix

source <name_of_venv>/bin/activate

Windows

.\<name_of_venv>\Scripts\activate

3. Install yawning-titan into the venv along with all of it's dependencies

python3 -m pip install -e .

This will install all the dependencies including algorithm libraries. These libraries all use torch. If you'd like to install tensorflow for use with Rllib, you can do this manually or install tensorflow as an optional dependency by postfixing the command in step 3 above with the [tensorflow] extra. Example:

python3 -m pip install -e .[tensorflow]

Development Installation

To install the development dependencies, postfix the command in step 3 above with the [dev] extra. Example:

python3 -m pip install -e .[dev]

Application Directories

Upon install, YT creates a set of application directories, both hidden for YT use, and visible for user use. The created directory trees for Linux, Windows, and MacOS operating systems are detailed below:

Linux

~/
├─ .cache/
│  ├─ yawning_titan/
│  │  ├─ log/
├─ .config/
│  ├─ yawning_titan/
├─ .local/
│  ├─ share/
│  │  ├─ yawning_titan/
│  │  │  ├─ app_images/
│  │  │  ├─ db/
│  │  │  ├─ docs/
├─ yawning_titan/
│  ├─ agents/
│  ├─ game_modes/
│  ├─ images/
│  ├─ notebooks/

Windows

~/
├─ AppData/
│  ├─ yawning_titan/
│  │  ├─ app_images/
│  │  ├─ config/
│  │  ├─ db/
│  │  ├─ docs/
│  │  ├─ logs/
├─ yawning_titan/
│  ├─ agents/
│  ├─ game_modes/
│  ├─ images/
│  ├─ notebooks/

MacOS

~/
├─ Library/
│  ├─ Application Support/
│  │  ├─ Logs/
│  │  │  ├─ yawning_titan/
│  │  │  │  ├─ log/
│  │  ├─ Preferences/
│  │  │  ├─ yawning_titan/
│  │  ├─ yawning_titan/
│  │  │  ├─ app_images/
│  │  │  ├─ db/
│  │  │  ├─ docs/
├─ yawning_titan/
│  ├─ agents/
│  ├─ game_modes/
│  ├─ images/
│  ├─ notebooks/

Documentation

YT comes with a full set of documentation created using the Sphinx documentation library and are hosted on GitHub pages at https://dstl.github.io/YAWNING-TITAN.

These docs can also be built manually from the cloned repo by using the following commands:

This will require the development dependencies to be installed, see Development Installation

Unix

cd docs
make html

Windows

cd docs
.\make.bat html

This will build the documentation as a collection of HTML files which uses the Read The Docs sphinx theme. Other build options are available but may require additional dependencies such as LaTeX and PDF. Please refer to the Sphinx documentation for your specific output requirements.

Example Notebooks

A collection of example notebooks have been provided. The original versions of these notebooks stored in yawning_titan/notebooks/_package_data and are copied over to the newly created users notebooks application directory (~/yawning_titan/notebooks) at install. These are the best place to start if you want to get a feel for YT before builidng the docs and exploring further. If the notebooks become corrupted in the users notebooks application directory, they can be reset running the following commands from an interactive Python session on your venv:

from yawning_titan.notebooks.jupyter import reset_default_jupyter_notebooks
reset_default_jupyter_notebooks(overwrite_existing=True)

Cite This Work

If you would like to include a citation for YT in your work, please cite the paper published at the ICML 2022 ML4Cyber Workshop.

@inproceedings{inproceedings,
 author = {Andrew, Alex and Spillard, Sam and Collyer, Joshua and Dhir, Neil},
 year = {2022},
 month = {07},
 title = {Developing Optimal Causal Cyber-Defence Agents via Cyber Security Simulation},
 maintitle = {International Confernece on Machine Learning (ICML)},
 booktitle = {Workshop on Machine Learning for Cybersecurity (ML4Cyber)}
}

License

YAWNING-TITAN is released under MIT license. Please see LICENSE for details.

Related Environments

You may also wish to explore the PrimAITE environment, an effective simulation capability for the purposes of training and evaluating AI in a cyber-defensive role: https://github.com/Autonomous-Resilient-Cyber-Defence/PrimAITE