Network intrusion detection technology based on deep learning

With the increasing network security threats, traditional network intrusion detection methods have shown numerous limitations in dealing with complex and evolving attacks. Deep learning technology, with its powerful feature learning and pattern recognition capabilities, provides a new solution for network intrusion detection. This research employs an improved Long Short-Term Memory (LSTM) model combined with attention mechanism to construct an intrusion detection system for industrial control networks. Experiments were conducted in SCADA system environment, using KDD99 dataset and actual industrial network traffic data for training and testing. The method outperforms traditional machine learning approaches in detection accuracy, recall rate, and false alarm rate, particularly demonstrating strong generalization ability in handling unknown attack types.