An RBF-PSO based approach for early detection of DDoS attacks in SDN

The architecture of SDN helps in early detection and mitigation of DDoS attacks, which is a challenge in the traditional network. With appropriate identification patterns and detection scheme, the SDN controller detects DDoS attacks in infant stages and suppresses their impact on the entire network. This paper proposes a feature set for classification of DDoS attacks from normal traffic. Further, a system model for detection of DDoS attacks implemented with SDN controller is proposed. The detection module in system model is based on RBF network having PSO optimized learning that is further compared with several schemes. The effectiveness of the system model has been verified by its real-time emulation with mininet emulator. The proposed system model is efficient in classifying heavy traffic load in the network from that of DDoS attacks and is capable of handling the DDoS attacks in their early stages itself.