Feature Sequencing Method of Industrial Control Data Set Based on Multidimensional Evaluation Parameters

Abstract

The industrial control data set has many features and large redundancy, which has a certain impact on the training speed and classification results of the neural network anomaly detection algorithm. However, features are independent of each other, and dimension reduction often increases the false positive rate and false negative rate. The feature sequencing algorithm can reduce this effect. In order to select the appropriate feature sequencing algorithm for different data sets, this paper proposes an adaptive feature sequencing method based on data set evaluation index parameters. Firstly, the evaluation index system is constructed by the basic information of the data set, the mathematical characteristics of the data set, and the association degree of the data set. Then, the selection model is obtained by the decision tree training with the data label and the evaluation index, and the suitable feature sequencing algorithm is selected. Experiments were conducted on 11 data sets, including Batadal data set, CICIDS 2017, and Mississippi data set. The sequenced data sets are classified by ResNet. The accuracy of the sequenced data sets increases by 2.568% on average in 30 generations, and the average time reduction per epoch is 24.143%. Experiments show that this method can effectively select the feature sequencing algorithm with the best comprehensive performance.