Enhancement in Securing Open Source SDN Controller Against DDoS Attack

The SDN network paradigm was developed as a solution to overcome limitations of traditional networks by separating the control and data planes, resulting in greater flexibility and scalability. However, its centralized architecture can become vulnerable to DDoS attacks, posing threat to network availability. To address this, the paper proposes the utilization of machine learning, specifically support vector machine to analyze flow table data and detect potentially malicious traffic as a countermeasure. By employing these techniques, SDN networks can detect and mitigate DDoS attacks, reducing their impact on network performance and availability. The efficacy of these techniques has been demonstrated through experimentation on the CIC-DDoS2019 dataset. Furthermore, future enhancements may include optimizing individual flows for DDoS and deploying the model to the SDN Cloud for use in public networks.