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>IEEE Standards>35 INFORMATION TECHNOLOGY. OFFICE MACHINES>35.240 Applications of information technology>35.240.50 IT applications in industry>IEEE 3121-2025 - IEEE Standard for Nonintrusive State Monitoring Framework for Embedded Devices in Industrial Control Systems
Released: 19.12.2025

IEEE 3121-2025

IEEE Standard for Nonintrusive State Monitoring Framework for Embedded Devices in Industrial Control Systems

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Standard number:IEEE 3121-2025
Released:19.12.2025
ISBN:979-8-8557-2731-9
Pages:29
Status:Active
Language:English
DESCRIPTION

IEEE 3121-2025

This standard defines a system framework of a nonintrusive state monitoring system. This standard specifies the functional modules of the nonintrusive state monitoring system, which includes analog and digital data collecting module, analog and digital data processing module, model training module, anomaly detection module and human-machine-interface module. Specifically, this standard also describes definitions, terminologies, module interfaces, and technical requirements of each module.

The purpose of this standard is to provide a framework for nonintrusive state monitoring systems to help ensure the security of embedded devices in industrial control systems (ICSs). The standard specifies the system design, functional modules and their interfaces, performance indicators of the nonintrusive state monitoring system, thus coordinate stakeholders could jointly complete the system.

New IEEE Standard - Active. A nonintrusive state monitoring framework for embedded devices in industrial control systems is provided in this standard. The techniques covered include analog signal acquisition, analog data processing, model training, anomaly detection, and human-machine interface. The analog signal acquisition is to gather various types of sensor signals from the embedded devices. The analog data processing is to process the collected data to extract features. The model training is to train a classification model that can identify different types of attacks. The anomaly detection is to implement, manage, and secure the detection engine. The human-machine interface is to provide alarm information and get feedback.