Anomaly detection using machine learning and adopted digital twin concepts in radio environments

Anomaly detection using machine learning and adopted digital twin concepts in radio environments

Abstract Reliable and secure wireless communication is essential in Industry 4.0. This work presents an anomaly detection framework using Digital Twin (DT) technology to simulate and monitor dynamic radio environments. By modeling network conditions and attack scenarios, the DT enables accurate iden...

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Bibliographic Details
Main Authors: Mohamed Hussien Moharam, Omar Hany, Ahmed Hany, Amenah Mahmoud, Mariam Mohamed, Sohila Saeed
Format: Article
Language:English
Published: Nature Portfolio 2025-05-01
Series:Scientific Reports
Subjects:
Digital twin (DT)
Received signal strength Indicator (RSSI)
Support vector machine (SVM)
Logistic regression (LR)
Random forest (RF)
Online Access:https://doi.org/10.1038/s41598-025-02759-5
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https://doi.org/10.1038/s41598-025-02759-5

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