Mitigation and Detection of DDOS attacks using Software Defined Network (SDN) and Machine Learning

• Published in: International journal for research in applied science and engineering technology 2023-04, Vol.11 (4), p.4821-4829
• Main Authors: R L, Hemanth kumar, K P, Bhargava, A R, Dr. Ashok Kumar
• Format: Article
• Language: English
• ISSN: 2321-9653; 2321-9653
• DOI: 10.22214/ijraset.2023.47344

An emerging networking paradigm called Software Defined Networking (SDN) enables network control to be restricted to a logically centralized controller. This makes network visibility and management simpler on a global scale. SDN is a suitable network model for extensive deployment in real-world settings since it can be programmed using high-level programming languages. SDN is still vulnerable to a number of network attacks, the most notable of which being the DDoS (distributed denial of service) attack. A DDoS attack has the potential to render the controller, the brain of SDN, inoperable. SDN security is still in its infancy, and both business and academics have done a lot of research in this area. The SDN DDoS mitigation strategy utilizing machine learning (ML) is the main topic of this study. This work also surveys network traffic aspects for DDoS detection