A Network Traffic Classification Method Based on Graph Convolution and LSTM
Yang Pan, Xiao Zhang, Hui Jiang, Cong Li
Abstract
In the identification of normal and abnormal traffic flows, Convolutional Neural Network (CNN) is commonly used to extract spatial features of network traffic at present. However, its limitation is that the one-dimensional form of traffic flow data needs to be converted into two-dimensional form, without considering the potential spatial correlation between traffic flows. In view of the potential correlation between network traffic flows, this paper proposes a classification method based on graph convolution and Long-Short Term Memory (LSTM). First, perform data preprocessing on the traffic flow data, then use the graph convolutional network to extract the spatial features of spatial topology and use LSTM to extract its temporal features. Finally, the performance of the algorithm is evaluated on the sampled UNSW-NB15 data set. Experimental results show that the proposed method can effectively extract the potential features of network traffic data. Compared with other methods such as feature selection, bidirectional LSTM (BiDLSTM) and CNN-LSTM, it proves the effectiveness of the proposed algorithm and performs better in classification performance.