Detection of DDoS Attack on SDN Control plane using Hybrid Machine Learning Techniques

Software Defined Network (SDN) provides a promising solution over traditional networks by decoupling the control plane and network plane. With the help of this feature, controller can get global view of the entire network. Since the controller acts as a core part of the SDN environment, there is a serious threat towards the controller in terms of security. A Distributed Denial of Service (DDoS) attack is the most potential attack in SDN environment. DDoS attack prevents the authorized user to access the available resources for infinite amount of time. In this paper, we have proposed the hybrid machine learning model to protect the controller from DDoS attacks. And our experimental results clearly manifest that the hybrid machine learning model provides more accuracy, detection rate and less false alarm rate compared to simple machine learning models.