Real-time DDoS attack detection based on deep learning - Chuanhuang LI; Zhengjun SUN; Xiaoyong YUAN; Xiaolin LI; Liang GONG; Weiming WANG (Dianxin kexue)
Distributed denial of service (DDoS) is a special form of denial of service (DoS) attack based on denial of service(DoS).It is a distributed,collaborative large-scale network attack.A DDoS detection method based on deep learning was presented.The method included two stages:feature processing and model detection:feature extraction,format conversion and dimension reconstruction of the input data packet was performed in feature processing stage;in the model detection stage,the processed features were input to the depth learning network model to detect whether the input data packets was DDoS attack packet.The model was trained by the ISCX2012 dataset,and the model was validated by real-time DDoS attack.The experimental results show that DDoS attack detection method based on deep learning has high detection precision,little dependency on hardware and software equipment,and the model of depth learning network is easy to update.