Research on network attack detection and defense based on deep learning algorithm

Traditional defense mechanisms have obvious limitations in the face of zero-day vulnerabilities, encrypted traffic attacks and counter-samples. With its automatic feature extraction and strong adaptability to high-dimensional data, deep learning provides a new solution for network attack and defense. In this paper, a CNN-BiLSTM-Attention hybrid neural network architecture is proposed, which integrates convolutional neural network (CNN), Bilstm and attention mechanisms, and is used to model the spatio-temporal characteristics of network attacks, which significantly improves the accuracy and recall rate of attack detection. At the same time, Focal Loss optimization loss function is introduced to solve the category imbalance problem, which effectively improves the model's ability to identify rare attack types. In terms of defense, this paper constructs a dynamic defense framework based on Deep Reinforcement Learning (DRL), which controls the attack response time within 50ms, and enhances the robustness of the model through confrontation training and model distillation technology, so that its defense success rate exceeds 85% in the face of FGSM and other confrontation attacks. The experimental results show that the detection model proposed in this paper performs well on multiple data sets, with an accuracy rate of 99.2% and a false alarm rate of only 0.3%, and shows significant advantages in dynamic defense and countermeasure robustness. This study provides an effective technical framework and practical example for AI-driven network security protection.