Abstract

The Industrial Internet of Things (IIoT) integrates physical systems with digital networks, unlocking immense operational efficiency but introducing critical cyber-physical security risks. Understanding these threats and developing robust defenses requires realistic testing environments. This Systematic Literature Review provides a dual focus: classifying the evolving landscape of IIoT cyberattacks and identifying the capabilities of current cybersecurity testbeds. The review categorizes testbed approaches across simulation, virtualization, and machine-learning-based detection frameworks. Crucially, our findings reveal a significant misalignment: current theoretical attack taxonomies are not adequately represented or testable within existing practical testbed implementations. We identify key research gaps, particularly regarding scalability and underrepresented industrial sectors, providing clear direction for the advancement of realistic IIoT security validation.