A Multi-Dimensional Feature Fusion Framework With XGBoost for IIoT-Driven Behavioral Analytics in Industrial Internet Systems

Abstract

Industrial Internet of Things (IIoT) systems generate massive behavioral data, demanding efficient analytics frameworks for real-time monitoring. This study proposes a multi-dimensional feature fusion framework integrating XGBoost, tailored for IIoT-driven behavioral pattern recognition. A four-dimensional architecture is constructed to analyze critical attributes across contact degree, status, duration, and social relations, leveraging edge-computed IIoT footprints (e.g., mobile signaling, network interaction data). The framework defines three behavioral modes and achieves 98.89% precision, 98.85% recall, and 98.85% F1-score via XGBoost. Feature importance analysis identifies key indicators such as mobile number status and interaction frequency. This work demonstrates the potential of harmonizing AI with IIoT data fusion, providing a scalable solution for real-time monitoring in Industrial Internet and future network architectures.