Design of Web Application Firewall System through Convolutional Neural Network and Deep Learning

Attacks against web applications emerge in endlessly, and protecting the security of web applications is an important part of the current cyberspace security. This paper constructs one cloud web application firewall system based on deep learning. The system architecture adopts a multi-server distributed architecture. Taking into account the real-time nature of web requests and the user's sense of user experience, through the use of a high-performance server framework Openers and high-efficiency volumes. The convolutional neural network improves the operating efficiency of the system while ensuring the recognition rate. At the same time, the system implemented in this paper also saves server request information in the database to further provide targeted data sets for artificial intelligence training.