When SDN comes up as a new technology, while it also brings many benefits such as high availability, scalability and performance, it also brings us new vulnerabilities that is targeted by attackers. Botnet Based DDoS Flooding Attacks have been one of the major problems for service provider networks who encountered these repeatedly since the first DDoS came into existence in the early 2000’s. In this thesis, we mainly concentrate on the source-based detection approach against Botnet Based DDoS Flooding Attack by combining the strength of SDN and s-Flow-RT technology. The main purpose of this research is to detect Botnet Based DDoS Flooding Attack that can also be performed in distributed SDN environments by using a similar approach with an available detection mechanism which is not implemented previously on an extended network with more network elements in order to observe whether the obtained successful results on the small network are compatible with a result obtained on this research. This study also includes a detection application using previously studied detection approach based on statistical inference model. The detection application is tested on virtual environments by organizing a Botnet Based DDoS Flooding Attacks on a predefined source node and then test results show that the mechanism could effectively detect the attack.