Marek Wolan e3d3a94da2 Merged PR 321: CAOS 0.8 observations
## Summary
* Remove the usecase-specific and agent-specific observation classes, replacing with a more flexible system
* Add configuration schemas to every observation class
* Add router, firewall, port, and application observation
* Re-shape the dict structure of observations to make it adhere to CAOS 0.8
* Change existing configs to use the new structure
* make host observation separate

## Test process
existing and new unit tests as well as ad hoc notebooks

## Checklist
- [ ] PR is linked to a **work item**
- [ ] **acceptance criteria** of linked ticket are met
- [ ] performed **self-review** of the code
- [ ] written **tests** for any new functionality added with this PR
- [ ] updated the **documentation** if this PR changes or adds functionality
- [ ] written/updated **design docs** if this PR implements new functionality
- [ ] updated the **change log**
- [ ] ran **pre-commit** checks for code style
- [ ] attended to any **TO-DOs** left in the code

Related work items: #2417
2024-04-02 14:00:27 +00:00
2024-02-06 18:58:50 +00:00
2023-08-15 13:28:02 +01:00
2023-06-02 12:59:01 +01:00
2024-01-30 09:56:16 +00:00

PrimAITE

image

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

Getting Started with PrimAITE

💫 Install & Run

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 >= 3.8 < 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 https://github.com/Autonomous-Resilient-Cyber-Defence/PrimAITE/releases/download/v2.0.0/primaite-2.0.0-py3-none-any.whl
primaite setup

Run:

primaite session

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 https://github.com/Autonomous-Resilient-Cyber-Defence/PrimAITE/releases/download/v2.0.0/primaite-2.0.0-py3-none-any.whl
primaite setup

Run:

primaite session

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 git@github.com: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]

6. Perform the PrimAITE setup:

primaite setup

📚 Building 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.

Description
ARCD Primary-Level AI Training Environment (PrimAITE)
Readme 21 MiB
Languages
Python 80.2%
Jupyter Notebook 19.8%