DDoS Attack Detection in SDN: Optimized Deep Convolutional Neural Network with Optimal Feature Set

This framework attempts to introduce a new Distributed denial-of-service (DDoS) attack detection and mitigation model. It is comprised of two stages, namely DDoS attack detection and mitigation. The first stage consists of three important phases like feature extraction, optimal feature selection, and classification. In order to optimally select the features of obtained feature sets, a new improved algorithm is implanted named Improved Update oriented Rider Optimization Algorithm (IU-ROA), which is the modification of the Rider Optimization Algorithm (ROA) algorithm. The optimal features are subjected to classification using the Deep Convolutional Neural Network (CNN) model, in which the presence of network attacks can be detected. The second stage is the mitigation of the attacker node. For this, a bait detection mechanism is launched, which provides the effective mitigation of malicious nodes having Distributed Denial-of-Service (DDoS) attacks. The experimentation is done on the KDD cup 99 dataset and the experimental analysis proves that the proposed model generates a better result which is 90.06% in mitigation analysis and the overall performance analysis of the proposed model on DDoS Attack Detection is 96% better than conventional methods.