  Distributed denial of service (DDoS) attack becomes a rapidly growing problem
with the fast development of the Internet. The existing DDoS attack detection
methods have time-delay and low detection rate. This paper presents a DDoS
attack detection method based on network abnormal behavior in a big data
environment. Based on the characteristics of flood attack, the method filters
the network flows to leave only the 'many-to-one' network flows to reduce the
interference from normal network flows and improve the detection accuracy. We
define the network abnormal feature value (NAFV) to reflect the state changes
of the old and new IP address of 'many-to-one' network flows. Finally, the DDoS
attack detection method based on NAFV real-time series is built to identify the
abnormal network flow states caused by DDoS attacks. The experiments show that
compared with similar methods, this method has higher detection rate, lower
false alarm rate and missing rate.
