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Distributed denial of service (DDoS) attack becomes a rapidly growing problemwith the fast development of the Internet. The existing DDoS attack detectionmethods have time-delay and low detection rate. This paper presents a DDoSattack detection method based on network abnormal behavior in a big dataenvironment. Based on the characteristics of flood attack, the method filtersthe network flows to leave only the 'many-to-one' network flows to reduce theinterference from normal network flows and improve the detection accuracy. Wedefine the network abnormal feature value (NAFV) to reflect the state changesof the old and new IP address of 'many-to-one' network flows. Finally, the DDoSattack detection method based on NAFV real-time series is built to identify theabnormal network flow states caused by DDoS attacks. The experiments show thatcompared with similar methods, this method has higher detection rate, lowerfalse alarm rate and missing rate.

DDoS Attack Detection Method Based on Network Abnormal Behavior in Big Data Environment