PrimAITE

The ARCD Primary-level AI Training Environment (PrimAITE) provides an effective simulation capability for the purposes of training and evaluating AI in a cyber-defensive role. It incorporates the functionality required of a primary-level ARCD environment, which includes:

• The ability to model a relevant platform / system context;

• The ability to model key characteristics of a platform / system by representing connections, IP addresses, ports, traffic loading, operating systems and services;

• Operates at machine-speed to enable fast training cycles.

PrimAITE presents the following features:

• Highly configurable (via YAML files) to provide the means to model a variety of platform / system laydowns and adversarial attack scenarios;

• A Reinforcement Learning (RL) reward function based on (a) the ability to counter the specific modelled adversarial cyber-attack, and (b) the ability to ensure success;

• Provision of logging to support AI evaluation and metrics gathering;

• Realistic network traffic simulation, including address and sending packets via internet protocols like TCP, UDP, ICMP, and others

• Routers with traffic routing and firewall capabilities

• Support for multiple agents, each having their own customisable observation space, action space, and reward function definition, and either deterministic or RL-directed behaviour

Whilst PrimAITE ships with a number of example modelled scenarios (a.k.a. Use Cases), it has not been developed to mandate the solving of a single cyber challenge, and instead provides a highly flexible environment application that can be extended and reconfigured by the user to suit their specific cyber defence training and evaluation needs. PrimAITE provides default networks, red agent and green agent behaviour, reward functions, and action / observation space configuration, all of which can be utilised out of the box, but which ultimately can (and in some instances should) be built upon and / or reconfigured to meet the needs of different defensive agent developers. The PrimAITE user guide provides comprehensive instruction on all PrimAITE features, functionality and components, and can be consulted in order to help guide users in any reconfiguration or enhancements they wish to undertake; a library of example Jupyter notebooks are also provided to support such work.

Getting Started with PrimAITE

💫 Installation

PrimAITE is designed to be OS-agnostic, and thus should work on most variations/distros of Linux, Windows, and MacOS. Currently, the PrimAITE wheel can only be installed from GitHub. This may change in the future with release to PyPi.

Windows (PowerShell)

Prerequisites:

• Manual install of Python >= 3.9 < 3.12

Install:

mkdir ~\primaite
cd ~\primaite
python3 -m venv .venv
attrib +h .venv /s /d # Hides the .venv directory
.\.venv\Scripts\activate
pip install primaite-{VERSION}-py3-none-any.whl[rl]
primaite setup

Unix

Prerequisites:

• Manual install of Python >= 3.8 < 3.12

sudo add-apt-repository ppa:deadsnakes/ppa
sudo apt install python3.10
sudo apt-get install python3-pip
sudo apt-get install python3-venv

Install:

mkdir ~/primaite
cd ~/primaite
python3 -m venv .venv
source .venv/bin/activate
pip install primaite-{VERSION}-py3-none-any.whl[rl]
primaite setup

Developer Install from Source

To make your own changes to PrimAITE, perform the install from source (developer install)

1. Clone the PrimAITE repository

git clone [email protected]:Autonomous-Resilient-Cyber-Defence/PrimAITE.git

2. CD into the repo directory

cd PrimAITE

3. Create a new python virtual environment (venv)

python3 -m venv venv

4. Activate the venv

Unix

source venv/bin/activate

Windows (Powershell)

.\venv\Scripts\activate

5. Install primaite with the dev extra into the venv along with all of it's dependencies

python3 -m pip install -e .[dev,rl]

6. Perform the PrimAITE setup:

primaite setup

Note

It is possible to install PrimAITE without Ray RLLib, StableBaselines3, or any deep learning libraries by omitting the rl flag in the pip install command.

Running PrimAITE

Use the provided jupyter notebooks as a starting point to try running PrimAITE. They are automatically copied to your PrimAITE notebook folder when you run primaite setup.

1. Activate the virtual environment

Windows (Powershell)

.\venv\Scripts\activate

Unix

source venv/bin/activate

2. Open jupyter notebook

python -m jupyter notebook

Then, click the URL provided by the jupyter command to open the jupyter application in your browser. You can also open notebooks in your IDE if supported.

📚 Documentation

Pre requisites

Building the documentation requires the installation of Pandoc

Unix

sudo apt-get install pandoc

Other operating systems

Follow the steps in https://pandoc.org/installing.html

Building the documentation

The PrimAITE documentation can be built with the following commands:

Unix

cd docs
make html

Windows (Powershell)

cd docs
.\make.bat html

Example notebooks

Check out the example notebooks to learn more about how PrimAITE works and how you can use it to train agents. They are automatically copied to your primaite installation directory when you run primaite setup.