Research on Deep Learning Model of Computer Image Vision Technology in Network Traffic Analysis

This study focuses on the application of computer image vision technology in network traffic analysis, and proposes an innovative deep learning model, which combines transfer learning and isolated forest algorithm to improve the accuracy and real-time detection of abnormal traffic in communication networks. Firstly, transfer learning strategy is adopted in this study, and a pre-trained deep neural network model is used as the starting point to quickly adapt to the traffic characteristics of a specific network environment. Then, the isolated forest algorithm is introduced as an anomaly detection mechanism, which can identify anomalies in data by building isolated trees, especially for high-dimensional data sets. By combining these two technologies, an efficient abnormal traffic detection system for communication networks is constructed in this study. The research team conducted a large number of simulation experiments, and the results show that the model can accurately identify abnormal traffic patterns in various network scenarios, and significantly improve the performance of the detection system. In addition, the model also shows good generalization ability when facing unknown types of attacks.