MTDSimTime

A time-based simulator used for evaluating Moving Target Defence (MTD) techniques.

Getting Started:

1. Installing conda
2. Creating conda environment
   • conda env create -f environment.yml
   • conda config --add channels conda-forge
3. Activating the environment
   • conda activate mtdsimtime
4. Updating the environment
   • conda env update --name mtdsimtime --file environment.yml --prune

Features

1. Network graph generation

2. Attack Operation

3. MTD Operation

4. Snapshot

5. Evaluation based on MTTC

System Architecture

The system uses the 3-layer HARM model to represent the network. This is a representation of the network, with the lowest levels on the bottom and the highest levels on the top:

layer	Description
Network	Made up of all the Hosts, connected in an Attack Graph, with exposed and un-exposed hosts that attackers will attempt to compromise
Host	Made up of several services (internal and external) in an Attack Graph. The host is compromised when an internal service is compromised
Services	An attack tree of vulnerabilities. A service is compromised when the sum of the vulnerabilities exploited impact is above 7
Vulnerabilities	Generated with a set Attack Complexity and Impact

more info: MTD parameter

Documents

see docs for all related documents.

Setup the previous works only

switch to another branch (MTDSim / New-Attack-Method) or go directly to:

MTDSim

MTDSimTze

This was all run on Python 3.9.13 64 Bit. In the root directory in terminal, run the following commands in your virtual environment to setup the environment:

• Setup virtualenv
  • python -m pip install virtualenv venv
  • python -m virtualenv venv
• Activate environemnt
  • source venv/bin/activate
• Install dependencies
  • python setup.py install
  • pip install -r requirements.txt
• Run an example: The following is only an example of how the function can be made, reference the run.py file or use the –help command to understand the parameters.
  • For New-Attack-Method:
    • python batchrun.py
  • For MTDSim:
    • python -m mtdnetwork.run -m IPShuffle -n 50 -e 10 -s 5 -l 3 results.json