Software-Defined Network (SDN) has emerged as the new big thing in networking. The separation of the control plane from the data plane and application plane gives SDN an edge over traditional networking. With SDN, the devices are configured at the control plane which makes it easier to manage network devices from one central point. However, decoupled architecture creates a single point of failure. A single point of failure attracts cyber-attacks, such as Distributed Denial of Service (DDoS) attacks. Attackers have recently been using multi-vector attacks from single-vector attacks. The need for real-time detection as a countermeasure is of paramount importance. The attackers using sophisticated techniques to launch DDoS attacks dictates the need for a sophisticated intrusion detection system. This paper proposes a Deep Neural Network (DNN) solution for real-time detection of DDoS attacks in SDN. The proposed IDS produced a detection accuracy of 97.59% using fewer resources and less time.

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