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Distributed Denial of Service (DDOS) attack is one of the most common networkattacks. DDoS attacks are becoming more and more diverse, which makes itdifficult for some DDoS attack detection methods based on single network flowcharacteristics to detect various types of DDoS attacks, while the detectionmethods of multi-feature DDoS attacks have a certain lag due to the complexityof the algorithm. Therefore, it is necessary and urgent to monitor the trend oftraffic change and identify DDoS attacks timely and accurately. In this paper,a method of DDoS attack detection based on deep belief network featureextraction and LSTM model is proposed. This method uses deep belief network toextract the features of IP packets, and identifies DDoS attacks based on LSTMmodel. This scheme is suitable for DDoS attack detection technology. The modelcan accurately predict the trend of normal network traffic, identify theanomalies caused by DDoS attacks, and apply to solve more detection methodsabout DDoS attacks in the future.

DDoS attack detection method based on feature extraction of deep belief network