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Software-Defined Networking (SDN) has made its place in the networks as new technology. SDN's programmable behavior enables it to change behavior on the fly, provides instructions for the task's automatic performance, dynamic scaling, and service integration. These advantages have made SDN necessary in networks. However, SDN suffers from the threat of DDoS attack. We have developed an approach to mitigate these threats by creating an ONOS Flood Defender Application (OFD App). This app effectively detects DDoS attack using supervised and ensemble machine learning techniques and mitigates them by tracebacking the attack traffic to its origin. Our results show that ensemble machine learning techniques perform better than single machine learning algorithm to detect DDoS attack. Random Forest classifier (RFC), an ensemble technique, performs best with the highest accuracy of 99.3% in detecting DDoS attack, followed by XGBoost classifier with an accuracy of 99%. The proposed framework of the OFD app is implemented in a Mininet emulator and ONOS SDN controller. The application performs effectively in terms of time, accuracy, and overhead of the system. Our app efficiently mitigates the attacks, thereby preventing a tremendous amount of damage to legitimate users.

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