Mitigation of DDoS Attacks in SDN Network

The ubiquity of Distributed Denial of Service (DDoS) attacks presents an escalating menace to network infrastructure, capable of inflicting severe disruptions on information and communication technology systems. Addressing this threat demands effective detection and mitigation strategies to curb their detrimental impact. However, traditional networks grapple with the cost and complexity of deploying robust hardware and software solutions for combatting these attacks. Software-Defined Networking (SDN), a transformative architectural paradigm that decouples the control and data planes, fostering enhanced scalability, flexibility, and network management. In this study, we introduce a sophisticated DDoS detection and mitigation framework entrenched within an SDN architecture, leveraging the predictive capabilities of the random forest machine-learning algorithm. This innovative system adeptly discerns normal traffic from malicious incursions, leveraging flow entries for classification. Upon identification of DDoS assaults, it dynamically adjusts traffic routing within switches to neutralize the threat. This study serves as a testament to the potency of amalgamating machine learning with SDN architecture to fortify network security against DDoS onslaughts.