Classification Method of Industrial Internet Intrusion Detection Based on Feature Selection

Due to the diversity and differences of industrial Internet access equipment, it is difficult to maintain and vulnerable to attacks. For this security problem, it is necessary to introduce relevant defense systems to identify various intrusion attacks. The traditional intrusion detection system can detect fewer types of attacks, and the network traffic data has poor classification performance due to the redundancy of irrelevant features. Therefore, we propose a classification method for industrial Internet intrusion detection based on feature selection. At first, this method preprocesses the dataset, and determines the strength of the feature by calculating the Pearson correlation coefficient of the feature, and determines the optimal threshold for feature extraction; then, from the perspective of machine learning and deep learning, logistic regression is used. Eight models including logistic regression, support vector machine, K-nearest neighbor, decision tree, random forest, multi-layer perceptron, convolutional neural network, and spatial-temporal network are respectively subjected to binary and multi-classification experiments and evaluated. The experimental results show that the binary classification effect of random forest is the best, and the multi-classification effect of decision tree is the best. Finally, the effectiveness of this method is verified in the real industrial Internet practice.