Deep Defense: Using Radial Basis Neural Networks for Mitigating the DDoS Attacks

The SDN architecture supports the detection and mitigation of DDoS attacks as soon as possible, which is difficult in a conventional network. The SDN controller identifies DDoS attacks in their early phases and mitigates their impact on the entire network using proper identification patterns and detection schemes. This study presents a feature set for distinguishing DDoS attacks from regular traffic. It provides a system paradigm for detecting DDoS assaults using an SDN controller. The system model's detection module is built on an RBF neural network, which is then compared to other methodologies. The proposed system model is capable of identifying high network traffic from DDoS attacks and dealing with DDoS attacks in their early stages. To handle extremely large DDoS traffic flows in Software Defined Networking (SDN), this study presents a rapid and effective DDoS detection method. The purpose of this study is to introduce new approaches for addressing DDoS threats in SDNs and to assist SDN controllers in managing excessive malicious traffic. This research explains several scenarios and examples where these tactics can be applied, and explores various studies on DDoS attacks. Furthermore, three different algorithms have been compared: (i) Quasi-Newton Gradient Algorithm, (ii) Gradient Descent with Momentum Algorithm, and (iii) Variable Learning Rate Gradient Descent Algorithm. A Radial Basis Function (RBF) neural network has been trained using these algorithms. During this research work, the NSL-KDD dataset was used. The findings show that, for a shorter training period, the Gradient Descent with Momentum Algorithm produces results with higher accuracy.