The security of industrial networks is crucial for areas such as energy,manufacturing,and infrastructure.A method designed on the basis of convolutional neural networks is proposed to address the security detection issues of industrial networks.Dur-ing the process,a one-dimensional convolutional neural network is used as the foundation of the model,and attention mechanisms and short-term memory mechanisms are added before the output layer of the model.The experimental results indicate that the cache data volume is below 59Mb when the total running time of the research method is 600s.The research method can have strong learning and prediction abilities in network security detection,learn effective features and patterns from a large amount of training data,distin-guish between normal and malicious behavior,avoid a large number of misoperations or interference with normal workflow due to false alarms,and complete the processing and analysis of a large amount of data in a short time,providing timely detection results,To pre-vent potential security threats as soon as possible.
维普
万方数据