@Article{cmc.2020.06177,
AUTHOR = {Jieren Cheng, Yifu Liu, Xiangyan Tang, Victor S. Sheng, Mengyang Li, Junqi Li},
TITLE = {DDoS Attack Detection via Multi-Scale Convolutional Neural Network},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {62},
YEAR = {2020},
NUMBER = {3},
PAGES = {1317--1333},
URL = {http://www.techscience.com/cmc/v62n3/38357},
ISSN = {1546-2226},
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.},
DOI = {10.32604/cmc.2020.06177}
}