Artificial intelligence-driven network attack prediction algorithms and their application to computer security

With the increasing frequency and complexity of cyber-attacks, traditional means of security protection have been difficult to meet the demand for real-time and high efficiency. Artificial intelligence, as a powerful tool, shows unique advantages in cyber attack prediction and defense. This paper focuses on the design and optimization of AI-driven cyber-attack prediction algorithms, proposes a deep learning-based prediction model, and combines data preprocessing techniques and model optimization strategies to enhance the performance of the algorithm. The study analyzes the practical application effect of the specific algorithm in intrusion detection system, threat intelligence analysis and industrial control system security, and verifies its remarkable ability in improving the accuracy of attack detection, reducing the rate of false alarms and realizing real-time warning. The research in this paper not only provides a new technical path for network security protection, but also lays a theoretical foundation for the application of artificial intelligence technology in a wider range of security fields.