Abstract: Network intrusion detection is an important research topic in the field of network security which is used to distinguish normal and abnormal network behaviors by analyzing traffic characteristics to realize intrusion traffic detection. To solve the problems of the complex feature extraction process,and insufficient information extraction in existing intrusion detection models,an intrusion detection model based on internal and external convolutional networks is proposed. Firstly,an one-dimensional convolutional neural network is used to extract the internal features of the traffic data. Then, an undirected homogeneous graph is obtained by calculating the similarity of the internal features. In addition the communication behavior of the traffic on the external network side is modeled as a directed heterogeneous graph,and graph convolutional network is used to learn embedding containing multiple interactive behaviors of network traffic from two graghs. Finally, the learned flow embedding is input into the classifier for final classification. Experimental results show that compared with existing methods,the detection accuracy and false alarm rate of the proposed model are better than those of the compared models.

Key words: intrusion detection, deep learning, graph convolutional network, convolutional neural network