Analysis of Artificial Intelligence in Internet Security

With the rapid development of artificial intelligence (AI) this year, it has become a key technology that can improve Internet security. Regarding network security, AI technology and its related tools have been widely used in intrusion detection, malware identification, and phishing attack defense. After reviewing the latest relevant literature, this paper reviews the current technical standards and uses the methods mentioned in the literature to analyze the advantages and limitations of AI in network security, and focuses on the challenges it faces in the face of adversarial attacks, data privacy leakage, and interpretable models. According to the research reviewed, although the current AI model has excellent performance in an ideal experimental environment, its reliability in the face of real environments or hostile conditions still has room for improvement. To this end, this paper deeply explores optimization directions such as adversarial training, federated learning, zero-shot learning, and interpretable AI, and puts forward future development trends and governance recommendations. Finally, according to the research, it can be concluded that the technological innovation of AI and network security needs to be paralleled with ethical supervision to achieve AI's reliable application in network security.