  Distributed Denial of Service (DDoS) is one of the most prevalent attacks
that an organizational network infrastructure comes across nowadays. We propose
a deep learning based multi-vector DDoS detection system in a software-defined
network (SDN) environment. SDN provides flexibility to program network devices
for different objectives and eliminates the need for third-party
vendor-specific hardware. We implement our system as a network application on
top of an SDN controller. We use deep learning for feature reduction of a large
set of features derived from network traffic headers. We evaluate our system
based on different performance metrics by applying it on traffic traces
collected from different scenarios. We observe high accuracy with a low
false-positive for attack detection in our proposed system.
