Research on Traffic Intrusion Detection Method Based on Deep Learning

With the rapid development of computer technology and the expansion of the Internet, intrusions on the Internet have become more frequent. With the development of machine learning technology, people apply machine learning technology to the anomaly detection of network traffic. However, traditional traffic classification not only relies on complex features, but also extracts users' private content, which has a negative impact on users. It is already difficult to meet the current increasingly large-scale network. Due to the rapid development of deep learning recently, it has very good applications in many fields. In this article, on the basis of it, we use Convolutional Neural Networks (CNN) and long- and short-term memory networks, and comprehensively put forward corresponding intrusion detection models based on the actual classification characteristics of the data set and model, optimization is performed.