Innovation of Network Attack Detection and Defense Strategies Based on Deep Learning

Abstract

With the rapid development of information technology today, the means of network attacks are becoming increasingly diverse and complex, posing a serious threat to network security. The aim of this study is to explore the innovative aspects of network attack detection and defense strategies through deep learning, in order to provide a new perspective and approach in the field of network security. Firstly, analyze the significance and challenges of research on network attack detection, as well as the application of deep learning in this field. Next, the article elaborates on a network attack detection model that is based on convolutional neural networks, bidirectional long short-term memory networks, attention mechanisms, and integrates multiple deep learning techniques. And based on this, further discuss innovative network attack defense strategies such as deep reinforcement learning, autoencoders, and Petri net modeling. These research results provide a new technological approach and new ideas for improving the accuracy and efficiency of network attack detection, and building intelligent network security protection systems.