DDoS Attack Detection via Multi-Scale Convolutional Neural Network |
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Authors: | Jieren Cheng Yifu Liu Xiangyan Tang Victor S Sheng Mengyang Li Junqi Li |
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Affiliation: | 1.School of Information Science and Technology, Hainan University, 570228, Haikou, China.
2 State Key Laboratory of Marine Resource Utilization in South China Sea, 570228, Haikou, China.
3 Department of Computer Science, University of Central Arkansas, Conway, AR 72035, USA. |
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Abstract: | Distributed Denial-of-Service (DDoS) has caused great damage to the network
in the big data environment. Existing methods are characterized by low computational
efficiency, high false alarm rate and high false alarm rate. In this paper, we propose a
DDoS attack detection method based on network flow grayscale matrix feature via multiscale convolutional neural network (CNN). According to the different characteristics of
the attack flow and the normal flow in the IP protocol, the seven-tuple is defined to
describe the network flow characteristics and converted into a grayscale feature by binary.
Based on the network flow grayscale matrix feature (GMF), the convolution kernel of
different spatial scales is used to improve the accuracy of feature segmentation, global
features and local features of the network flow are extracted. A DDoS attack classifier
based on multi-scale convolution neural network is constructed. Experiments show that
compared with correlation methods, this method can improve the robustness of the
classifier, reduce the false alarm rate and the missing alarm rate. |
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Keywords: | DDoS attack detection convolutional neural network network flow feature extraction |
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