SDN (Software-Defined Networking) revolutionized network administration by splitting the control plane from the data plane, allowing for centralized control also improved network flexibility. However, SDN also introduces security vulnerabilities, particularly Distributed Denial of Service (DDoS) attacks, which can significantly disrupt network services. This research introduces a novel two-phase method to improve the security of SDN environments through the use of SHAP (Shapley Additive Explanations). To mitigate the risk of unauthorized access, initial phase entails the establishment of a secure authentication mechanism that prevents unauthorized nodes from accessing network services. The second phase is dedicated to the mitigation & detection of DDoS attacks that are initiated by verified nodes. A SHAP (Shapley Additive Explanations)-based detection system is created, trained on a variety of datasets, including CICDDoS2019, and incorporated into the SDN controller for real-time traffic analysis. The efficiency of the proposed methodology in enhancing detection accuracy, reducing false positive rates, and maintaining network performance is demonstrated through comprehensive evaluations using simulation tools. This investigation offers a solution that is adaptable, efficient, and robust to enhance the detection and mitigation of DDoS in SDN environments.