GNN-ASG: A Double Feature Selection-based Adversarial Sample Generation Method in Industrial Control System

Due to the unique constraints of industrial control data, industrial control adversarial sample attacks are particularly challenging. Existing methods strive to conduct adversarial sample attacks under the conditions of satisfying industrial control data constraints, however, the results are not ideal. Therefore, this study proposes a new adversarial sample generation method GNN-ASG based on double feature selection. GNN-ASG uses data constraints to ensure the rationality of generated data, and uses Graph Deviation Network (GDN) and Autoencoder to improve the quality and versatility of adversarial samples. A new adversarial sample evaluation metrics Adversarial Sample Attack Impact Rate (ASAIR) is proposed to address the problem that existing evaluation metrics are difficult to accurately judge the effectiveness of adversarial sample attacks. This method considers the principle and application environment of adversarial samples, and fully demonstrates the practical effect of adversarial samples. In a comprehensive experiment conducted on three public datasets, GNN-ASG achieves an impressive ASAIR of 21.83%, higher than existing methods of 13.94%. This paper demonstrates the versatility and effectiveness of GNN-ASG by comparing its performance with three state-of-the-art adversarial sample generation methods on four anomaly detection models. GNN-ASG can maximally reduce the F1-Score of the detection model by 0.7605.