Research on Network Security Threat Detection and Defense Mechanism Based on Artificial Intelligence

With the openness, anonymity and cross-border characteristics of cyberspace, network security incidents occur frequently, and the network security challenges in the era of artificial intelligence (AI) are becoming increasingly severe. This paper analyzes the application of AI technology in improving the accuracy and efficiency of network intrusion detection, constructing adaptive defense mechanism and data leakage prevention strategy. The theoretical basis relies on machine learning (ML), deep learning (DL) and reinforcement learning (RL), which provide mathematical and algorithmic support for building an efficient and intelligent network security system. This paper designs a comprehensive network security threat detection and defense system framework, including network traffic data preprocessing, feature extraction, malware detection, network abnormal behavior identification and defense strategy optimization. The experimental results show that the model based on DL has achieved high accuracy in malware detection and network abnormal behavior identification, while the defense strategy optimized by Q-learning has effectively reduced the attack success rate and false alarm rate.