The Intrusion Detection Method for Power Grid Industrial Control Systems Based on Improved Triplet Neural Network

With the rapid advancement of artificial intelligence, particularly deep learning, intrusion detection methods based on deep learning have gained significant attention. Deep learning can automatically extract features from large datasets and classify complex patterns, offering better adaptability and flexibility than traditional methods. It is especially effective in handling dynamic industrial environments and unknown attacks, improving detection accuracy while reducing false positives and false negatives. However, challenges remain, such as model generalization and computational efficiency. To address these, this paper proposes an improved triplet neural network-based intrusion detection method for industrial control systems (ICS). The system includes modules for data collection, preprocessing, model construction, training, and real-time detection. By incorporating a self-attention mechanism, the improved model better recognizes complex attack patterns and adapts to new threats. Experimental results show an accuracy of 98.7%, with a significant reduction in false positives and false negatives, enhancing ICS security and providing a new approach for handling future cyberattacks.