Intelligent DDoS Attack Prevention and Mitigation in SDN Environment

DDoS attacks are among the significant challenges to the stability and reliability of networks, especially in SDN environments. The proposed solution gives advanced approach for intelligent DDoS attack prevention and mitigation using state-of-the-art deep learning techniques. By leveraging a DNN-based classification model trained on diverse network traffic datasets, the proposed method accurately identifies malicious traffic patterns in real time. The framework provides integration of optimization strategies related to performance and improves accuracy of detection of DDoS attacks along with reductions in false positives and allows for scalability. Experimental results indicate the efficacy of the presented method in comparison to others with respect to precision, recall, and computational efficiency on the detection and mitigation of DDoS attacks. This study identifies critical issues in mitigating DDoS and offers a viable solution towards safe SDN operations.