Home

GAR-Project 2019/2020

The purpose of this project is to develop an artificial intelligence to classify possible DDoS attacks in an SDN network. This will be done by using data collectors such as Telegraf, Mininet to emulate the SDN network, and InfluxDB and Grafana as a means to store data and visualize it respectively. For non-English speakers we leave part of the content of this guide written in Spanish:

• Network Scenario - Mininet Guide: Link
• DDoS using hping3 tool Guide: Link
• Mininet Internals (II) Guide: Link

Keywords: DDoS attacks; SDN network; Artificial Intelligence classification; Mininet

Index

• Installation methods 🔧
  • Vagrant
  • Native
• Our scenario
  • Running the scenario
  • Is working properly?
• Attack time! 💥
  • Time to limit the links
  • Getting used to hping3
  • Installing things... again! 😩
  • Usage
  • Demo time! 🎉
• Traffic classification with a SVM (Support Vector Machine)
  • First step: Getting the data collection to work 😵
  • Second step: Generating the training datasets
  • Third step: Putting it all together: src/traffic_classifier.py
• Mininet CLI (Command Line Interface)
• Mininet Internals
  • Network Namespaces
• Mininet Internals (II)
  • Is Mininet using Network Namespaces?
  • The Big Picture
    • How would our Kernel-level scenario look then?
• Troubleshooting
• Appendix
  • The Vagrantfile
  • File descriptors: stdout and friends

Note

Throughout the document we will always be talking about 2 virtual machines (VMs) on which we implement the scenario we are discussing. In order to keep it simple we hace called one VM controller and the other one test. Even though the names may seem kind of random at the moment we promise they're not. Just keep this in mind as you continue reading.