Chen Zhang1, Jieren Cheng1,2,3,*, Xiangyan Tang1, Victor S. Sheng4, Zhe Dong1, Junqi Li1
CMC-Computers, Materials & Continua, Vol.61, No.2, pp. 657-675, 2019, DOI:10.32604/cmc.2019.06207
Abstract Distributed denial of service (DDoS) attacks launch more and more frequently and are more destructive. Feature representation as an important part of DDoS defense technology directly affects the efficiency of defense. Most DDoS feature extraction methods cannot fully utilize the information of the original data, resulting in the extracted features losing useful features. In this paper, a DDoS feature representation method based on deep belief network (DBN) is proposed. We quantify the original data by the size of the network flows, the distribution of IP addresses and ports, and the diversity of packet sizes of More >
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