Handling intrusion and DDoS attacks in Software Defined Networks using machine learning techniques

Software-Defined Networking (SDN) is an emerging concept that intends to replace traditional networks by breaking vertical integration. It does so by separating the control logic of network from the underlying switches and routers, suggesting logical centralization of network control, and allowing to program the network. Although SDN promises more flexible network management, there are numerous security threats accompanied with its deployment. This paper aims at studying SDN accompanied with OpenFlow protocol from the perspective of intrusion and Distributed Denial of Service (DDoS) attacks and suggest machine learning based techniques for mitigation of such attacks.