DDoS Attack Detection in SDN Environment using Bi-directional Recurrent Neural Network
Vinay Itagi, Mayur Javali, H. Madhukeshwar, Pooja Shettar, P. Somashekar, D. G. Narayan · 2021
Software Defined networking (SDN) is an emerging technology for effectively managing the network resources. SDN architecture has two planes namely control and data plane. Control plane manages the network using global view of the network topology and data plane helps routing and forwarding of packets. Centralised nature of controller poses security threats to SDN environment. Distributed Denial of Service (DDoS) attack is the most popular cyber attack which can cause economic loss due to network disruption. Thus, the design of DDoS detection system which can detect the attacks accurately in SDN environment is an important research issue. The purpose of this study is to develop a real-time method for detecting DDoS attacks using a bi-directional recurrent neural network (BRNN). We use deep learning models for the classification of DDoS attacks with real-time SDN data.Results demonstrated that BRNN has greater accuracy than feed forward neural network when we use Mininet emulator to create SDN environment.