Application of Convolution Neural Network in Network Abnormal Traffic Detection

Abstract

With the development of network scale, network technology affects every aspect of people's life. It is of great significance to detect network intrusion. Traditional research is mostly based on open data sets, the open data sets lack timeliness, and the validity of the research results is unknown. Based on the previous research, this paper proposed a novel intrusion detection method based on convolutional neural network. Firstly, real abnormal data packets were obtained by building a network environment and using real network attack tools. Second, abnormal data packets were used to generate features. Furthermore those futures are transformed into gray images for visual analysis. In order to evaluate effectiveness and superiority of proposed method, several evaluating indicators were introduced. The experimental result shows that precision, recall and F1 value of the proposed method reached 0.99, 0.99 and 0.99 respectively, which were all superior to the traditional machine learning methods.