Securing IoE Environments with Semantic Data Stream Analysis and Behavioral Fingerprinting

In the landscape of Industry 5.0, Internet of Everything (IoE) networks are emerging as crucial components for connecting diverse industrial sensors and devices, expanding beyond traditional IoT boundaries to integrate people, processes, and data. However, this increased connectivity raises significant security concerns, as the growing complexity of IoE environments introduces new attack vectors and privacy risks. Additionally, the integration of heterogeneous devices and data sources presents both technical and semantic interoperability challenges, requiring robust mechanisms for meaningful data interpretation and secure exchange. This paper, developed within the HOMEY project, presents an architecture for gathering and monitoring semantic data streams in IoE environments, addressing both interoperability and security challenges. Our approach leverages Knowledge Graphs to represent sensor metadata, locations, access rights, and operational contexts, enabling dynamic stream monitoring and data querying. An approach based on Federated Learning allows distributed behavioral fingerprinting of IoE devices, which is exploited on top of the platform to perform anomaly detection from real-time data streams. The approach enhances reliable, privacy-preserving anomaly detection, contributing to the security and resilience of next-generation industrial IoE ecosystems.