Machine Learning-Driven Anomaly Detection for Supply Chain Integrity in 5G Industrial Automation Systems
Ugoaghalam Uche James
Abstract
The rapid deployment of 5G-enabled industrial automation systems has transformed global supply chains by enabling ultra-reliable low-latency communication (URLLC), real-time decision-making, and seamless integration of cyber-physical systems. However, the increased connectivity and data exchange across stakeholders have also introduced new risks of cyberattacks, data manipulation, and operational disruptions that threaten supply chain integrity. Machine learning (ML)-driven anomaly detection has emerged as a powerful approach to safeguarding these systems by identifying unusual behaviors, malicious activities, and performance deviations in complex industrial networks. This review explores the convergence of 5G technology, industrial automation, and advanced ML algorithms—including supervised, unsupervised, and deep learning models—for anomaly detection in supply chain operations. The study highlights key methodologies such as federated learning for decentralized monitoring, reinforcement learning for adaptive responses, and graph-based neural networks for interdependency mapping within supply chain ecosystems. Furthermore, the paper investigates the challenges of handling high-dimensional heterogeneous data, ensuring interpretability of ML outputs, and integrating anomaly detection with existing security frameworks. Case studies and recent applications are examined to demonstrate practical benefits, including fraud prevention, counterfeit detection, and real-time logistics monitoring. The review also emphasizes policy, governance, and ethical considerations in deploying ML-driven solutions in mission-critical 5G industrial settings. Ultimately, this paper provides a comprehensive synthesis of current advances, gaps, and future research opportunities, positioning ML-based anomaly detection as a cornerstone for resilient, secure, and trustworthy supply chain integrity in the era of 5G-enabled industrial automation.