Hierarchical Testing with Rabbit Optimization for Industrial Cyber-Physical Systems

TL;DR

This paper introduces HERO, a hierarchical adversarial testing framework utilizing Rabbit Optimization to evaluate and improve the robustness of deep learning-based Prognostics and Health Management systems in industrial cyber-physical systems, especially for dynamic fuel cell applications.

Contribution

HERO is a novel black-box adversarial testing method that generates physically constrained adversarial examples using Artificial Rabbit Optimization, applicable across diverse ICPS scenarios.

Findings

HERO uncovers vulnerabilities in advanced PHM models.

It demonstrates the effectiveness of Rabbit Optimization in generating realistic adversarial examples.

The framework enhances understanding of model robustness in complex industrial environments.

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.