# Adversarial Attacks and Defenses in Fault Detection and Diagnosis: A Comprehensive Benchmark on the Tennessee Eastman Process

arXiv: https://arxiv.org/abs/2403.13502 · HTML: https://mlsanity.com/paper/2403.13502/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 65 references that are in the map.
- First posted on arXiv 20 Mar 2024.

## Builds on (most cited first, 20 of 20)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Towards Evaluating the Robustness of Neural Networks](https://mlsanity.com/paper/1608.04644/) (2016)
- [Membership Inference Attacks against Machine Learning Models](https://mlsanity.com/paper/1610.05820/) (2016)
- [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018)
- [DeepFool: a simple and accurate method to fool deep neural networks](https://mlsanity.com/paper/1511.04599/) (2015)
- [Evasion Attacks against Machine Learning at Test Time](https://mlsanity.com/paper/1708.06131/) (2017)
- [Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks](https://mlsanity.com/paper/1511.04508/) (2015)
- [Countering Adversarial Images using Input Transformations](https://mlsanity.com/paper/1711.00117/) (2017)
- [MagNet: a Two-Pronged Defense against Adversarial Examples](https://mlsanity.com/paper/1705.09064/) (2017)
- [Adversarial Example Detection for DNN Models: A Review and Experimental Comparison](https://mlsanity.com/paper/2105.00203/) (2021)
- [Scaleable input gradient regularization for adversarial robustness](https://mlsanity.com/paper/1905.11468/) (2019)
- [Data Poisoning Attacks Against Multimodal Encoders](https://mlsanity.com/paper/2209.15266/) (2022)
- [Gradient Adversarial Training of Neural Networks](https://mlsanity.com/paper/1806.08028/) (2018)
- [SADA: Semantic Adversarial Diagnostic Attacks for Autonomous Applications](https://mlsanity.com/paper/1812.02132/) (2018)
- [Conflicting Interactions Among Protection Mechanisms for Machine Learning Models](https://mlsanity.com/paper/2207.01991/) (2022)
- [DI-AA: An Interpretable White-box Attack for Fooling Deep Neural Networks](https://mlsanity.com/paper/2110.07305/) (2021)
- [Adversarial Attacks on Time-Series Intrusion Detection for Industrial Control Systems](https://mlsanity.com/paper/1911.04278/) (2019)
- [Data-Free Hard-Label Robustness Stealing Attack](https://mlsanity.com/paper/2312.05924/) (2023)
- [An Incremental Gray-box Physical Adversarial Attack on Neural Network Training](https://mlsanity.com/paper/2303.01245/) (2023)

## Built on by (most cited first, 2 of 2)

- [Quantum enhanced ensemble GANs for anomaly detection in continuous biomanufacturing](https://mlsanity.com/paper/2508.21438/) (2025)
- [Adapting Noise-Driven PUF and AI for Secure WBG ICS: A Proof-of-Concept Study](https://mlsanity.com/paper/2510.22283/) (2025)

## Abstract (arXiv)

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.