Abnormal Flow Monitoring of Industrial Control Network Based on Neural Network

Aiming at the network security problem caused by the rapid development of network, this paper uses a network traffic anomaly detection method of industrial control system based on convolutional neural network. In the traditional machine learning algorithm, the processing of features has a high impact on the performance of the model, and the model is highly dependent on features. This method uses the characteristics of convolutional neural network to autonomously learn features, which avoids this problem. In order to verify the superiority of the model, this paper takes accuracy as the evaluation index, and compares it with the traditional machine learning algorithm. The results show that the overall accuracy of the method is 99.88 %, which has higher accuracy than traditional machine learning algorithms such as decision tree algorithm (ID3), adaptive boosting tree (Adboost) and naive Bayesian model. Therefore, this method can be better applied to the anomaly detection of network traffic in industrial control system, and has practical application value.