Simulation of network traffic risk of enterprise cloud financial system by using deep learning

Abstract

Traditional network traffic analysis methods fall short in addressing the requirements of complex network environments. Therefore, the introduction of advanced technologies, such as deep learning, is necessary to enhance the accuracy and efficiency of network traffic analysis. This paper designs a risk analysis method of enterprise cloud financial system network traffic based on deep learning to improve the level and effect of network security assurance. A deep learning based traffic analysis framework has been constructed, which includes several main steps such as data preprocessing, feature extraction, model training, and result evaluation. The test results on the experimental data set show that this method can identify network attacks and abnormal traffic more quickly and accurately than the traditional network traffic analysis methods, and has better practicability and application prospects.