The wide proliferation of telecommunication technologies in the last decade also gives rise to many sophisticated security threats. Software-Defined Networking (SDN) is a new networking architecture that isolates the network control plane from the data plane that offers better features and functionalities to detect and deal with those security threats. Its programmable elastic feature permits efficient network management and provides network operators with the flexibility to monitor and fine-tune their network. However, the new technology is not free from new security concerns. The Distributed Denial of Service (DDoS) attack is one of the major concerns that mainly targets the SDN controller and threatens the security of the SDN networks. Since the controller is the key and focal component of the SDN, any problem occurring at the controller may degrade or even collapses the entire network. Therefore, there is a dire need for an effective approach to detect low rate DDoS attacks with high accuracy and low false positive rate. Thus, this thesis proposes an efficient DDoS attack detection approach called Generalized Entropy-Based Approach with a Dynamic Threshold to Detect DDoS Attacks on Software-Defined Networking Controller (GEADDDC). GEADDDC generalizes the Renyi Joint Entropy algorithm and uses a dynamic threshold to detect DDoS attacks on the SDN controller.