Hierarchical Testing With Rabbit Optimization for Industrial Cyber-Physical Systems

Hu, Jinwei, Tang, Zezhi, Jin, Xin, Zhang, Benyuan, Dong, Yi ORCID: 0000-0003-3047-7777 and Huang, Xiaowei ORCID: 0000-0001-6267-0366 (2025) Hierarchical Testing With Rabbit Optimization for Industrial Cyber-Physical Systems IEEE TRANSACTIONS ON INDUSTRIAL CYBER-PHYSICAL SYSTEMS, 3. pp. 472-484. ISSN 2832-7004, 2832-7004

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Abstract

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.

Item Type:	Article
Uncontrolled Keywords:	Adversarial testing, prognostics and health management, industrial cyber-physical systems, artificial rabbit optimization, Adversarial testing, prognostics and health management, industrial cyber-physical systems, artificial rabbit optimization
Divisions:	Faculty of Science & Engineering Faculty of Science & Engineering > School of Computer Science & Informatics Faculty of Science & Engineering > School of Computer Science & Informatics > Artificial Intelligence Faculty of Science & Engineering > School of Computer Science & Informatics > School of Computer Science & Informatics
Depositing User:	Symplectic Admin
Date Deposited:	05 Dec 2025 09:40
Last Modified:	23 May 2026 10:25
DOI:	10.1109/TICPS.2025.3586988
Related Websites:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3195874
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