Machine Learning-Based Anomaly Detection in Industrial IoT (IIoT) Systems

Abstract

Industrial IoT (IIoT) systems generate vast amounts of data, making anomaly detection crucial for identifying operational inefficiencies and security threats. This paper explores ML techniques like clustering, autoencoders, and time-series forecasting for anomaly detection in IIoT environments. It discusses challenges such as scalability, false positives, and real-time processing. Case studies highlight successful implementations in manufacturing, logistics, and energy sectors, demonstrating improved fault detection, enhanced security, and reduced operational risks.