Depth Analysis on Recognition and Prevention of DDoS Attacks via Machine Learning and Deep Learning Strategies in SDN

Recent years have witnessed the exponential growth of global digitization. Similarly, the rise in Distributed Denial of Service (DDoS) attacks continues unabated. DDoS is a significant threat to network security in the flexible environments of software-defined networks (SDN). This research discusses the study of the advanced characteristics of machine learning and deep learning technologies to detect and prevent DDoS attacks in SDNs. In this article, we closely examine the design of SDNs and the unique challenges they face during DDoS attacks. This research study analyzes various deep learning and machine learning models designed to respond to complex DDoS attack patterns and respond instantly to attacks. Different datasets related to attacks in SDN and IoT are discussed in the research article. Additionally, a comparative study of these methods are presented.