A Novel CNN-LSTM Fusion-Based Intrusion Detection Method for Industrial Internet

Abstract

Industrial internet security incidents occur frequently, and it is very important to accurately and effectively detect industrial internet attacks. In this paper, a novel CNN-LSTM fusion model-based method is proposed to detect malicious behavior under industrial internet security. Firstly, the data distribution is analyzed with the help of kernel density estimation, and the Pearson correlation coefficient is used to select the strong correlation feature as the model input. The one-dimensional convolutional neural network and the long short-term memory network respectively extract the spatial sequence features of the data and then use the softmax function to complete the classification task. In order to verify the effectiveness of the model, it is evaluated on the NSL-KDD dataset and the GAS dataset, and experiments show that the model has a significant performance improvement over a single model. In the detection of industrial network traffic data, the accuracy rate of 97.09% and the recall rate of 90.84% are achieved.