A Deep-Learning-Based Intrusion Detection Model Optimization Method for Dynamic Protection of New-Energy Power Stations

As the proportion of new-energy power stations (such as wind and solar power stations) in electricity production continues to increase, building a dynamic protection system for new-energy power stations is an important security protectio measure designed to address the ever-changing network threats. This paper proposes an optimization method for the dynamic protection of new energy power stations based on a deep-learning intrusion detection system model. This method efficiently optimizes the intrusion detection model by designing real-time traffic collection and event database, combined with knowledge distillation technology, thereby enhancing new energy power stations' network security protection capability.