Optimized deep neural network based DDoS attack detection and bait mitigation process in software defined network

Abstract

SummaryThe software‐defined network (SDN) is a new network design with an operating system that allows better network quality control. The controller's primary role in an SDN network is to divide the control and forwarding planes in order to provide essential network power. The backup controller in an SDN may confront a variety of obstacles during a DDoS attack. It disrupts the flow of the network by attacking the service nodes hence obstructing the legitimate users from getting service. To overcome these issues, this work introduces a DDoS attack detection model that will aid in removing the attacker from the network. Initially, the input data is sent into the detection phase, which identifies the existence and kind of attacks. The presence of an attack is determined based on the data flow, and statistical features. With the reference of the extracted features, the optimized deep neural network (DNN) will decide the existence of attacker in the network. To make the decision more precise, the training of DNN will be carried out by self‐improved moth flame optimization (SIMFO) Algorithm via tuning the optimal weights. Once an attacker's presence has been identified, it is critical to remove the attacker's node from the network.