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https://github.com/santhisenan/SDN_DDoS_Simulation

An attempt to detect and prevent DDoS attacks using reinforcement learning. The simulation was done using Mininet.
https://github.com/santhisenan/SDN_DDoS_Simulation

ddos-detection ddos-simulation ddpg-agent mininet openvswitch reinforcement-learning ryu sdn tensorflow

Last synced: 6 days ago
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An attempt to detect and prevent DDoS attacks using reinforcement learning. The simulation was done using Mininet.

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# DDoS Simulation in a Software Defined Network
This project aims to provide a basic framework for DDoS mitigation using Deep reinforcement learning. The network is implemented using Mininet (based on Software defined networking).

The design of the solution is inspired by the work "Deep Reinforcement Learning based Smart Mitigation of DDoS Flooding in Software-Defined Networks" by Yandong Liu and others [here](https://ieeexplore.ieee.org/abstract/document/8514971).

## Getting Started
Clone the repository
```
git clone https://github.com/santhisenan/SDN_DDoS_Simulation.git
```
## Prerequisites
Install dependencies

* Install Mininet
* Install OpenVSwitch
* Install Ryu
* Install Tensorflow
* Install Keras

* Clone ryu repository and copy ryu/ryu folder to SDN_DDoS_Simulation root

## Testing
Modify simple_tree_top.py according to test purpose
```
cd SDN_DDoS_Simulation
python simple_tree_top.py
```
Open a new Terminal tab
```
PYTHONPATH=. ryu/ryu/bin/ryu-manager main.py
```
## Running
```
cd SDN_DDoS_Simulation
python tree_topology.py
```
Open a new Terminal tab
```
PYTHONPATH=. ryu/ryu/bin/ryu-manager main.py
```

## Built With

* [Ryu Controller](https://osrg.github.io/ryu/) - Controller Framework for SDN
* [Mininet](https://maven.apache.org/) - SDN simulator
* [OpenVSwitch](http://mininet.org/) - Custom switch for SDN
* [Tensorflow](https://www.tensorflow.org/) - Deep Learning Framework
* [Keras](https://keras.io/) - Deep Learning Framework

## Authors

* Santhisenan Ajith
* Vishnu Kaimal
* Mohammed Musthafa K
* Ankith Madusudanan

## License

This project is licensed under the MIT License - see the [LICENSE.md](LICENSE.md) file for details