Guardians of Reliability, Robustness, and Resilience: Adversarial Maintenance in the Era of Industry 4.0 and 5.0
Vagan Terziyan, Olena Kaikova
Abstract
As Industry 4.0 evolves into Industry 5.0, enabling cyber-physical-social systems, the imperative emerges to ensure the reliability, robustness, and resilience of sophisticated industrial systems and smart assets. This paper introduces the concept of “Adversarial Maintenance” as a strategic paradigm that goes beyond the layered analytics and conventional maintenance practices. Our approach integrates Artificial Intelligence (AI), adversarial Machine Learning (ML), Complementary AI, digital immunity, and digital vaccination, among other technological enablers, to proactively anticipate, simulate, and counteract intentional adversarial threats. Going beyond descriptive, diagnostic, preventive, predictive, and prescriptive maintenance approaches, the suggested enablers explore the role of Adversarial Maintenance in preparing both technical systems and human individuals and groups for adversarial challenges. Adversarial pre-training, dynamic response mechanisms, and realistic adversarial scenario generation form the backbone of this approach, establishing a resilient defense against evolving cyber threats for complex and mission-critical cyber-physical-social systems. The proposed framework acts as a safeguard for Industry 4.0 and 5.0 ecosystems, ensuring not only reliability in operations but robustness and resilience in the face of intentional disruptions. As the guardians of these critical attributes, Adversarial Maintenance suggests a proactive and efficient strategy for securing the future of intelligent industrial systems.