This project implements a Anomaly detection and mitigation system using a machine learning model within the Ryu SDN Controller. The system monitors network traffic, detects potential DDoS attacks, and blocks malicious traffic while allowing legitimate traffic to pass through.

• Linux Operating System (Note: This project is designed to work on Linux)
• Python 3.9
• Mininet
• Ryu
• Scapy
• IPFS

Install Ryu:

sudo apt-get install ryu
pip install ryu

Install Mininet:

sudo apt-get install mininet

Clone the Repository

git clone [email protected]:MustangBro7/Federeated-Anomaly-Detection-System.git
cd Federeated-Anomaly-Detection-System

Create a virtual environment using venv

python3.9 -m venv ryu-python3.9-venv

Activate the virtual environment

source ryu-python3.9-venv/bin/activate

Install the required Python packages listed in requirements.txt

pip install -r requirements.txt

python training.py

Start the Ryu application

ryu-manager ddos_with_model.py

Open a new terminal and start Mininet with a simple topology

sudo -E mn --controller=remote,ip=127.0.0.1 --topo=single,4 --mac --switch=ovsk

To simulate a DDoS attack, you can use the following command from one of the hosts in Mininet (e.g., h1)

In the mininet intereface run the following command

python3 attack_simulation.py

• Ensure you are running on a Linux environment for compatibility with Mininet.
• The provided Python script uses a machine learning model trained to detect DDoS attacks. Ensure you have the required model files in the specified paths.

For more details on Ryu and Mininet, refer to their official documentation:

• Ryu Documentation
• Mininet Documentation