Introduction

Rapid changes in the digital technology landscape have significantly transformed industrial processes, driven by the deep integration of physical and digital components in production environments, resulting in the emergence of Cyber-Physical Systems (CPS). The remarkable potential of data analytics techniques applied to CPSs spans various domains, including cost reduction in maintenance, mitigating machine faults, minimizing repair downtime, optimizing spare parts inventory, extending spare part lifespan, increasing overall production, improving operator safety, verifying repairs, and enhancing profitability. However, the practical implementation of machine learning and deep learning approaches in real-world applications is constrained by their data-heavy requirements. 

Anomaly detection methods have been proposed in different domains in recent years, but traditional approaches are not directly applicable to ensure CPS security due to the increasing complexity and sophistication of attacks. The growing volume of data and the need for domain-specific knowledge challenge these methods, necessitating innovative solutions that integrate advanced artificial intelligence models with diverse sources of information, such as IoT sensor measurements and network data. 

Moreover, the rise of cyber-physical attacks, exemplified by incidents like Triton and Stuxnet, introduces novel and challenging issues. These attacks can deceive monitoring platforms, highlighting the need for advanced predictive maintenance techniques that leverage AI and deep learning to analyze specific industrial equipment features. Such techniques can uncover symptoms of potential failures, including those caused by malicious activities. 

To address these challenges, this special issue aims to investigate and analyze emerging trends in AI-based Anomaly Detection for Cyber-Physical Systems. We invite contributions focusing on advanced modeling and mining techniques, harnessing the power of AI and deep learning to detect anomalies in CPS. We welcome both theoretical advancements and application-oriented studies that explore the development of novel approaches based on advanced optimization techniques and learning paradigms, such as online learning, reinforcement learning, and deep learning. By advancing our understanding of complex phenomena in Cyber-Physical Systems, these contributions will contribute to the development of explainable AI models for Anomaly Detection in CPS.