Abstract
The Industrial Internet of Things (IIoT) and Smart Manufacturing environments face significant challenges in machine identity authentication, crucial for ensuring data integrity and cybersecurity. Traditional methods like One-Class Support Vector Machines (OCSVM) often fall short in sensitivity, specificity, and explainability. This research introduces a novel Functional Data Analysis (FDA)-based model that leverages unique temporal patterns in machine-generated data to authenticate machine identities, adapting to equipment wear and tear. Through a comprehensive comparative analysis, the FDA model demonstrates superior performance and robustness in industrial settings. Additionally, we propose integrating this FDA-based approach with the MQTT 5.0 protocol, enhancing data security in IIoT communications. This work not only advances IIoT cybersecurity but also supports digital twins and predictive maintenance, contributing to a more secure and efficient Smart Manufacturing ecosystem.
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