Detection and Classification Approach of Denial of Service Attack in SDN

Distributed Denial of Service (DDoS) attacks are a serious risk to network security and stability because they overload systems with malicious traffic, disrupting services. Conventional detection methods frequently find it difficult to keep up with the growing complexity and scope of these types of attacks. The use of machine learning (ML) and artificial intelligence (AI) approaches for efficient DDoS attack detection and categorization is examined in this study. AI/ML models can detect abnormalities in network traffic and differentiate between malicious and legitimate activity in real time by utilizing sophisticated data analytics and pattern recognition. The study offers a thorough examination of numerous feature extraction methods, supervised and unsupervised learning algorithms, and training and assessment datasets.Experimental findings show that AI/ML-based techniques outperform traditional techniques in terms of detection accuracy, scalability, and adaptability. The results demonstrate how intelligent systems may improve network defenses and lessen the effects of DDoS attacks in a variety of operating settings.