DomainCAT (Domain Connectivity Analysis Tool)

"See Connections Between Domains Right Meow"

The Domain Connectivity Analysis Tool is used to analyze aggregate connectivity patterns across a set of domains during security investigations

This project was a collaborative effort between myself and Matthew Pahl

Introduction

When analyzing pivots during threat hunting, most people approach it from the perspective of “what can a single pivot tell you?” But often actors will set their domains up to use commodity hosting infrastructure, so the number of entities associated with a given pivot are so big they don’t really give you any useful information.

This is where DomainCAT can help. Actors make decisions around domain registration and hosting options when setting up their malicious infrastructure. These can be considered behavioral choices.

• What registrar(s) do they use?
• What TLDs do they prefer?
• What hosting provider(s) do they like?
• What TLS cert authority do they use?

All of these decisions, together, makeup part of that actor’s infrastructure tools, tactics and procedures (TTPs), and we can analyze them as a whole to look for patterns across a set of domains.

But wait there's more

Introducing InfraCAT

What if instead of nodes being domains, they were the infrastructure and the edges were the connected domains? That was the thought process with InfraCAT. By seeing clusters of infrastructure, you can see tightly coupled groups of domains based on the infrastructure they use.

DomainCAT and InfraCAT are tools written in Jupyter Notebooks, a web-based interactive environment that lets you combine text, code, data, and interactive visualizations into your threat hunting toolbelt. The tool analyzes aggregate connectivity patterns across a set of domains looking at every pivot for every domain, asking; what are the shared pivots across these domains, how many shared pivots between each domain, do they have a small pivot count or a really large one? All of these aspects are taken into consideration as it builds out a connectivity graph that models how connected all the domains in an Iris search are to each other.

Example Visualizations:

3D visualization of domain to domain connections based on shared infrastructure, registration and naming patterns

2D visualization of domain to domain connection

3d visualization of infra to infra connection

DomainCat Tutorial

Click here for the DomainCAT Tutorial documentation

Installation Steps: Docker (recommended)

Note: building the container takes a bit of RAM to compile the resources for the jupyterlab-plotly extension. Bump up your RAM in Docker preferences to around 4Gb while building the container. Then afterwards you can drop it back down to your normal level to run the container

Steps:

Clone the git repository locally

$ git clone https://github.com/DomainTools/DomainCAT.git

Change directory to the domaincat folder

$ cd domaincat

Build the jupyter notebook container

$ docker build --tag domaincat .

Run the jupyter notebook

$ docker run -p 9999:9999 -v $(PWD)/data:/src/data --name domaincat domaincat

Mounting the data directory as a volume allows you to add new files to the container without having to rebuild it.

Installation Steps: Manual (cross your fingers)

Note: this project uses JupyterLab Widgets, which requires nodejs >= 12.0.0 to be installed...which is on you

Steps:

Clone the git repository locally

$ git clone https://github.com/DomainTools/DomainCAT.git

Change directory to the domaincat folder

$ cd domaincat

Install python libraries

$ pip install -r requirements.txt

JupyterLab widgets extension

$ jupyter labextension install jupyterlab-plotly@4.14.3 --no-build
$ jupyter labextension install @jupyter-widgets/jupyterlab-manager --no-build
$ jupyter labextension install plotlywidget@4.14.3 --no-build
$ jupyter lab build

Run the jupyter notebook

$ jupyter lab

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Release Notes:

October 25, 2021:

• Initial support for InfraCAT

August 24, 2021:

• Adding a way to remove domains in the graph that you aren't interested in (look at the bottom of the notebook)
• Refactor of the backend data structures to be a bit more efficient

April 27, 2021:

• Added support for dotenv to store REST API credentials in a .env file
• Added logic to support
  • comma delimited list of domains
  • domains defanged with square brackets

April 23, 2021:

• Added config flag to only analyze active domains
• Show count of selected domains

April 19: 2021:

• Bug fix to not normalize risk scores values when calculating node color
• Mo'better sorting of selected domains

April 15, 2021:

• Bug fix: wrong json element returned when querying search hash

April 14, 2021:

• Added UI to search either a list of domain names or an Iris search hash
• Added UI to enter Iris REST API username and password

April 7, 2021:

• Initial commit

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Plotly Bug: in the 2D visualization of the domain graph there is a weird bug in Plotly Visualization library where if your cursor is directly over the center of a node, the node's tool tip with the domain's name will disappear and if you click the node, it unselects all nodes. So only click on a node if you see it's tool tip