Adversarial Attacks and Defenses in Automated Control Systems: A Comprehensive Benchmark

arXiv:2403.13502v1 Announce Type: cross
Abstract: Integrating machine learning into Automated Control Systems (ACS) enhances decision-making in industrial process management. One of the limitations to the widespread adoption of these technologies in industry is the vulnerability of neural networks to adversarial attacks. This study explores the threats in deploying deep learning models for fault diagnosis in ACS using the Tennessee Eastman Process dataset. By evaluating three neural networks with different architectures, we subject them to six types of adversarial attacks and …

adoption adversarial adversarial attacks arxiv attacks automated benchmark control control systems cs.cr cs.lg cs.sy decision defenses eess.sy industrial industry limitations machine machine learning making management networks neural networks process process management study systems technologies threats vulnerability