用兔子优化生成真实物理约束的对抗样本，检测工业系统故障预测模型漏洞。
Hierarchical Testing with Rabbit Optimization for Industrial Cyber-Physical Systems
- 基于兔优化算法生成符合物理规律的对抗样本。
- 在质子交换膜燃料电池上暴露先进模型的脆弱性。
- 适合工业界提升智能维护系统的抗干扰能力。
本文提出 HERO（Hierarchical Testing with Rabbit Optimization），一种新型黑盒对抗测试框架，用于评估工业网络物理系统中基于深度学习的故障预测与健康管理（PHM）系统的鲁棒性。借助人工兔优化算法，HERO 从全局与局部视角生成符合物理约束且贴近真实数据分布的对抗样本，具备跨多种工业场景的泛化能力。研究聚焦于质子交换膜燃料电池系统，因其运行条件高度动态、退化机制复杂，且作为可持续高效能源解决方案正日益融入工业网络物理系统。实验结果表明，HERO 能够发现即使是先进 PHM 模型中的潜在漏洞，凸显了在实际应用中增强鲁棒性的紧迫性。通过应对这些挑战，HERO 展示了在广泛工业网络物理系统领域推动更稳健 PHM 系统的潜力。
原文摘要 · Abstract (English)
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.
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