the calssification of traffic flows in today's IP network has become an important research area with the adopation of machine learning techniques and Software Defined Network (SDN) Principles. Traditional methodologies including identifying traffic based on port number and payload inspection are not effective due to the dynamic and encrypted nature of current traffic. This project will attempt to utilize Supervised and Unsupervised ML Algorithms to classify flows based on packet and Byte information.
Build Topology
Setup virtualBox with Host, Switch, Controller VM
Create Internal network as underlay network
Configure overlay network
Simulation Traffic Flows
Use simulation tools to send various traffic flows between hosts
Modify controller scripts to output required data
Collect Training Data
Write scripts to collect output of controller monotoring application
Train Models
Using Jupyter Notebook, train and test supervised and unsupervised Machine Learning Models.
Use Models Real Time
Usung Models created in Notebook, Classify traffic real time from data collected from Ryu App.