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A tool that allows you to create vulnerable instrumented local or cloud environments to simulate attacks against and collect the data into Splunk

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Splunk Attack Range

Purpose

The Attack Range is a detection development platform, which solves three main challenges in detection engineering. First, the user is able to build quickly a small lab infrastructure as close as possible to a production environment. Second, the Attack Range performs attack simulation using different engines such as Atomic Red Team or Caldera in order to generate real attack data. Third, it integrates seamlessly into any Continuous Integration / Continuous Delivery (CI/CD) pipeline to automate the detection rule testing process.

The lab infrastructure contains a Windows Domain Controller, Windows Server, Windows Workstation and a Kali Machine, which comes pre-configured with multiple security tools and logging configuration. Furthermore, a Splunk server and a Phantom server completes the lab infrastructure. The lab infrastructure can be deployed locally or in the cloud. The building process is completely automated and can be executed with a single command.

The Attack Range incorporates different simulation engines such as Atomic Red Team from Red Canary and Caldera from Mitre. Attacks can be executed with a single command allowing the user to attack the lab infrastructure. These data can be used to develop new detections or test existing detection rules.

The differentiation to existing open source tools such as Detection Lab is that the Attack Range exposes all actions over the command line. This allows the user to integrate the Attack Range into a CI/CD pipeline and automating the detection testing/development process. For example, after writing a new detection for credential dumping, the Attack Range can automatically test it by performing the following steps: build Attack Range, perform attack T1103 (credential dumping), run detection, destroy Attack Range.

Demo

📺 A short demo (< 6 min) which shows the basic functions of the attack range. It builds a testing enviroment using terraform, walks through the data collected by Splunk. Then attacks it using MITRE ATT&CK Technique T1003 and finally showcases how ESCU searches are used to detect the attack.

Attack Range Demo

Deployment

Attack Range can be built in three different ways:

  • local using vagrant and virtualbox
  • in the cloud using terraform and AWS
  • cloud optimized using terraform, packer and AWS

Logical Diagram

Architecture

Attack Range consists of:

  • Windows Domain Controller
  • Windows Server
  • Windows Workstation
  • A Kali Machine
  • Splunk Server
  • Phantom Server

Which can be added/removed/configured using attack_range.conf. More machines such as Phantom, Linux server, Linux client, MacOS clients are currently under development.

Logical Diagram

Configuration

Running

Attack Range supports different actions:

  • Build Attack Range
  • Perform Attack Simulation
  • Search with Attack Range
  • Destroy Attack Range
  • Stop Attack Range
  • Resume Attack Range

Build Attack Range

  • Build Attack Range
python attack_range.py -m terraform/vagrant -a build

Perform Attack Simulation

  • Perform Attack Simulation
python attack_range.py -m terraform/vagrant -a simulate -st T1117,T1003 -t attack-range-windows-domain-controller

Search with Attack Range

  • Run a savedsearch and return the results
python attack_range.py -m terraform/vagrant -a search -sn search_name

Destroy Attack Range

  • Destroy Attack Range
python attack_range.py -m terraform/vagrant -a destroy

Stop Attack Range

  • Stop Attack Range
python attack_range.py -m terraform/vagrant -a stop

Resume Attack Range

  • Resume Attack Range
python attack_range.py -m terraform/vagrant -a resume

Cloud Optimized

Using the Attack Range for automated detection testing in a Continuous Integration (CI) pipeline, needs the ability to build it quickly. Therefore we introduced the mode cloud optimized by combining packer and terraform. In this mode you need to build the AMIs with packer and then use terraform with the prebuilt AMIs:

  • Build AMIs with packer
python attack_range.py -m packer -a build_amis
  • Build Attack Range with terraform and -ami flag:
python attack_range.py -m terraform -a build -ami
  • Deregister AMIs with packer
python attack_range.py -m packer -a destroy_amis

Features

Planned features

  • Linux Server
  • Linux Client
  • macOS Client

Support

Please use the GitHub issue tracker to submit bugs or request features.

If you have questions or need support, you can:

Author

Contributors

Contributing

We welcome feedback and contributions from the community! Please see our contribution guidelines for more information on how to get involved.

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A tool that allows you to create vulnerable instrumented local or cloud environments to simulate attacks against and collect the data into Splunk

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