Anomaly Classification in Industrial Internet of Things (2025)
ABSTRACT : This thesis presents an IIoT Anomaly Classification Framework designed to detect and categorize anomalies, including failures, attacks, and other significant events. The research addresses the critical need for robust anomaly detection and classification in IIoT systems by providing a comprehensive and scalable solution adaptable to various industrial contexts. The framework enhances modern industrial operations’ reliability, security, and efficiency, paving the way for more resilient and intelligent IIoT systems.