# Hierarchical Testing with Rabbit Optimization for Industrial Cyber-Physical Systems

arXiv: https://arxiv.org/abs/2507.04100 · HTML: https://mlsanity.com/paper/2507.04100/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 63 references that are in the map.
- First posted on arXiv 5 Jul 2025.

## Builds on (most cited first, 24 of 24)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [iTransformer: Inverted Transformers Are Effective for Time Series Forecasting](https://mlsanity.com/paper/2310.06625/) (2023)
- [DeepXplore: Automated Whitebox Testing of Deep Learning Systems](https://mlsanity.com/paper/1705.06640/) (2017)
- [Recent Advances in Adversarial Training for Adversarial Robustness](https://mlsanity.com/paper/2102.01356/) (2021)
- [Data Augmentation Can Improve Robustness](https://mlsanity.com/paper/2111.05328/) (2021)
- [Generating Natural Adversarial Examples](https://mlsanity.com/paper/1710.11342/) (2017)
- [Physical Adversarial Examples for Object Detectors](https://mlsanity.com/paper/1807.07769/) (2018)
- [Potential, Challenges and Future Directions for Deep Learning in Prognostics and Health Management Applications](https://mlsanity.com/paper/2005.02144/) (2020)
- [A Statistical Approach to Assessing Neural Network Robustness](https://mlsanity.com/paper/1811.07209/) (2018)
- [Consistency Regularization for Adversarial Robustness](https://mlsanity.com/paper/2103.04623/) (2021)
- [Towards Generating Real-World Time Series Data](https://mlsanity.com/paper/2111.08386/) (2021)
- [DeepCloak: Masking Deep Neural Network Models for Robustness Against Adversarial Samples](https://mlsanity.com/paper/1702.06763/) (2017)
- [Distribution-Aware Testing of Neural Networks Using Generative Models](https://mlsanity.com/paper/2102.13602/) (2021)
- [Amortized Reparametrization: Efficient and Scalable Variational Inference for Latent SDEs](https://mlsanity.com/paper/2312.10550/) (2023)
- [Reliability Assessment and Safety Arguments for Machine Learning Components in System Assurance](https://mlsanity.com/paper/2112.00646/) (2021)
- [Counterexample-Guided Data Augmentation](https://mlsanity.com/paper/1805.06962/) (2018)
- [Coverage Guided Testing for Recurrent Neural Networks](https://mlsanity.com/paper/1911.01952/) (2019)
- [Crafting Adversarial Examples for Deep Learning Based Prognostics (Extended Version)](https://mlsanity.com/paper/2009.10149/) (2020)
- [Hierarchical Distribution-Aware Testing of Deep Learning](https://mlsanity.com/paper/2205.08589/) (2022)
- [Distribution Awareness for AI System Testing](https://mlsanity.com/paper/2105.02540/) (2021)
- [On the Robustness of Explanations of Deep Neural Network Models: A Survey](https://mlsanity.com/paper/2211.04780/) (2022)
- [Universal Adversarial Attack on Deep Learning Based Prognostics](https://mlsanity.com/paper/2109.07142/) (2021)
- [Survey on Foundation Models for Prognostics and Health Management in Industrial Cyber-Physical Systems](https://mlsanity.com/paper/2312.06261/) (2023)
- [A temporal scale transformer framework for precise remaining useful life prediction in fuel cells](https://mlsanity.com/paper/2504.08803/) (2025)

## Built on by (most cited first, 4 of 4)

- [Stop Reducing Responsibility in LLM-Powered Multi-Agent Systems to Local Alignment](https://mlsanity.com/paper/2510.14008/) (2025)
- [Guided Action Flow: Q-Guided Inference for Flow-Matching Vision-Language-Action Policies](https://mlsanity.com/paper/2607.02092/) (2026)
- [Deep Reinforcement Learning Optimization for Uncertain Nonlinear Systems via Event-Triggered Robust Adaptive Dynamic Programming](https://mlsanity.com/paper/2512.15735/) (2025)
- [DDSA: Dual-Domain Strategic Attack for Spatial-Temporal Efficiency in Adversarial Robustness Testing](https://mlsanity.com/paper/2601.14302/) (2026)

## Abstract (arXiv)

This paper presents HERO (Hierarchical Testing with Rabbit Optimization), a novel black-box adversarial testing framework for evaluating the robustness of deep learning-based Prognostics and Health Management systems in Industrial Cyber-Physical Systems. Leveraging Artificial Rabbit Optimization, HERO generates physically constrained adversarial examples that align with real-world data distributions via global and local perspective. Its generalizability ensures applicability across diverse ICPS scenarios. This study specifically focuses on the Proton Exchange Membrane Fuel Cell system, chosen for its highly dynamic operational conditions, complex degradation mechanisms, and increasing integration into ICPS as a sustainable and efficient energy solution. Experimental results highlight HERO's ability to uncover vulnerabilities in even state-of-the-art PHM models, underscoring the critical need for enhanced robustness in real-world applications. By addressing these challenges, HERO demonstrates its potential to advance more resilient PHM systems across a wide range of ICPS domains.