The "SDNShield" project is dedicated to advancing the state-of-the-art in DDoS (Distributed Denial of Service) attack detection within the context of Software-Defined Networking (SDN). DDoS attacks remain a significant threat to network infrastructure, making it crucial to develop effective mitigation strategies. Leveraging the flexibility and programmability of SDN, we have devised an innovative approach to enhance network security.
1. Machine Learning-Based Detection: Our project employs machine learning techniques to analyze network traffic patterns in real-time. By training our models on a carefully curated and tailored dataset, we aim to identify and differentiate between legitimate network traffic and malicious DDoS attacks with a high degree of accuracy.
2. Custom Dataset: To facilitate our research, we have developed a specialized dataset that reflects real-world network traffic scenarios using mininet and ryu. This bespoke dataset encompasses a wide range of DDoS attack types and normal network activities, enabling comprehensive training and evaluation of our detection algorithms.
3. SDN Integration: "SDNShield" seamlessly integrates with SDN technologies, leveraging the OpenFlow protocol. This integration allows us to dynamically reconfigure network flows, diverting or mitigating traffic during detected attacks, thereby enhancing network resilience.
4. Open Source Collaboration: We believe in the power of open source collaboration and have made our project available on GitHub. Researchers, network administrators, and security professionals can explore our codebase, replicate experiments, and contribute to the ongoing improvement of DDoS detection in SDN environments.
Our primary objective is to provide a robust and adaptable solution for DDoS attack detection in SDN. By combining machine learning, a custom dataset, and SDN capabilities, we aim to enhance the security and reliability of network infrastructures. We invite the research community to engage with "SDNShield" and contribute to the evolution of network security in the face of evolving DDoS threats.
Before proceeding with the installation, make sure you have the following prerequisites installed on your system:
OS: Ubuntu 16.04 LTS or Ubuntu 18.04 LTS
Python : 3.6/3.8 and 2.7 (Default in OS required)
Python 3.6/3.8 as python3
Python 2.7 as python2
The repository contains CICFlowMeter, which includes corrected code and is fully functional for our research.
To install the required tools and dependencies, follow these steps:
git clone https://github.com/akd3070/SDN-DDoS-Detection-Research.git &&
cd SDN-DDoS-Detection-Research &&
sudo chmod +x Install.sh &&
./Install.shAll The required tools are installed and there depandiancies
After Sucessfully Running the Script
''' ryu-manager ryu.app.simple_switch '''
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