Research on Network Security Intrusion Detection Based on Devnet

With the promotion of industrial 4.0 and intelligent manufacturing, the network security of industrial control system becomes particularly important. In the face of new network attacks, the traditional intrusion detection system (IDS) has been difficult to meet the protection requirements, so it is particularly urgent to research and develop efficient network security detection technology. In this study, an intrusion detection method of industrial control system based on DevNet network is proposed, which uses deep convolution neural network architecture to detect events and locate evidence by analyzing the characteristics of key frames, which significantly improves the performance of multimedia event detection. This method optimizes the anomaly score through end-to-end learning, makes effective use of the limited marked abnormal data, and enhances the accuracy and real-time performance of detection. The research first examines the challenges of network security in industrial control systems, and then describes the DevNet framework and its core algorithms, including exception score learner, normal a priori and Z-Score deviation loss function. Through the experimental evaluation of multiple data sets, DevNet performs well in key evaluation indicators such as AUC-ROC and AUC-PR, especially in dealing with large-scale network traffic and defending against zero-day attacks. The interpretability of DevNet provides strong support for security analysis and enhances the credibility of the model prediction. This research not only enhances the protection of network security, but also promotes technological innovation, and provides new ideas for the field of network security. With the increase of IIoT equipment, DevNet is expected to become the key technology to protect the safety of industrial control system. The results of this research are of great significance in theory, and show broad prospects in practical application, and contribute to the progress of network security.