Attack Sample Generation Algorithm Based on Dual Discriminant Model in Industrial Control System

Abstract

The research of intrusion anomaly detection in industrial control system is facing the problem of few attack samples. This paper proposes an attack data sample generation algorithm based on dual discriminant model. Firstly, a few attack samples are generated by multiple attacks based on the original data set. Secondly, the sparse matrix algorithm is used to expand the data set, and the original data features are distributed to each extended data to ensure the authenticity of the seed data. Finally, a large number of attack samples are generated by the dual discriminant model to complete the sample expansion. The experimental results of Mississippi SCADA natural gas pipeline data set and an industrial control system data set show that, based on keeping the data in a reasonable range, the algorithm can obtain an effectively expanded sample data set. SVM, Random Forest and XGBOOST are used to classify the dataset. The results show that AUC index is better than the negative sample data set generated by traditional GAN algorithm, and is similar to the initial negative sample index.