故障检测与诊断中的对抗攻击与防御：基于田纳西-伊斯曼过程的综合基准研究
Adversarial Attacks and Defenses in Fault Detection and Diagnosis: A Comprehensive Benchmark on the Tennessee Eastman Process
AI总结：
本文基于田纳西-伊斯曼过程数据集，系统评估了三种神经网络在六种对抗攻击下的脆弱性，比较五种防御方法，并提出组合多种防御的新保护方案，为工业故障诊断中的机器学习安全提供基准与见解。
摘要：
将机器学习集成到自动化控制系统（ACS）中，可增强工业过程管理中的决策能力。这些技术在工业界广泛采用所面临的限制之一，是神经网络对对抗攻击的脆弱性。本研究利用田纳西-伊斯曼过程数据集，探讨了在ACS中部署深度学习模型进行故障诊断所面临的威胁。通过评估三种不同架构的神经网络，我们使其遭受六种类型的对抗攻击，并探索了五种不同的防御方法。我们的结果凸显了模型对对抗样本的强烈脆弱性，以及防御策略效果的不一致性。我们还提出了一种新颖的保护方法，即结合多种防御方法，并证明了其有效性。这项研究为确保工业过程中机器学习的安全性、实现鲁棒的故障诊断贡献了若干见解。
英文摘要：
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 explore five different defense methods. Our results highlight the strong vulnerability of models to adversarial samples and the varying effectiveness of defense strategies. We also propose a novel protection approach by combining multiple defense methods and demonstrate it's efficacy. This research contributes several insights into securing machine learning within ACS, ensuring robust fault diagnosis in industrial processes.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
