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Novel DDoS Feature Representation Model Combining Deep Belief Network and Canonical Correlation Analysis

Chen Zhang1, Jieren Cheng1,2,3,*, Xiangyan Tang1, Victor S. Sheng4, Zhe Dong1, Junqi Li1

College of Information Science & Technology, Hainan University, Haikou, 570228, China.
State Key Laboratory of Marine Resource Utilization in South China Sea, Haikou, 570228, China.
Key Laboratory of Internet Information Retrieval of Hainan Province, Hainan University, Haikou, China.
Department of Computer Science, University of Central Arkansas, Conway, AR 72035, USA.

*Corresponding Author: Jieren Cheng. Email: email.

Computers, Materials & Continua 2019, 61(2), 657-675. https://doi.org/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 different protocols and train the DBN in an unsupervised manner by these quantified values. Two feedforward neural networks (FFNN) are initialized by the trained deep belief network, and one of the feedforward neural networks continues to be trained in a supervised manner. The canonical correlation analysis (CCA) method is used to fuse the features extracted by two feedforward neural networks per layer. Experiments show that compared with other methods, the proposed method can extract better features.

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Cite This Article

C. Zhang, J. Cheng, X. Tang, V. S. Sheng, Z. Dong et al., "Novel ddos feature representation model combining deep belief network and canonical correlation analysis," Computers, Materials & Continua, vol. 61, no.2, pp. 657–675, 2019.

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cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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