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<p>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 identification of anomalies, particularly security threats. This study integrates machine learning with anomaly detection frameworks to enhance wireless network security. The proposed approach creates a virtual representation of the wireless environment, enabling accurate identification of anomalies and security threats. To validate the effectiveness of this framework, multiple machine learning algorithms based on traditional classifiers which are compared for their ability to detect anomalies, particularly jamming attacks. XGBoost achieved the highest accuracy (0.99) and perfect detection (1.00) of normal traffic and signal drift, outperforming Random Forest (0.98), Support Vector Machine (0.97), Logistic Regression (0.93), and K Nearest Neighbors (0.81). These results highlight XGBoost as a reliable solution for wireless network security. This work contributes to ongoing research on the integration of DT for comprehensive wireless network monitoring, emphasizing their potential to improve anomaly detection and resilience in next-generation communication systems.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月26日

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  • Mohamed Hussien Moharam,
  • Omar Hany,
  • Ahmed Hany,
  • Amenah Mahmoud,
  • Mariam Mohamed,
  • Sohila Saeed

摘要

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 identification of anomalies, particularly security threats. This study integrates machine learning with anomaly detection frameworks to enhance wireless network security. The proposed approach creates a virtual representation of the wireless environment, enabling accurate identification of anomalies and security threats. To validate the effectiveness of this framework, multiple machine learning algorithms based on traditional classifiers which are compared for their ability to detect anomalies, particularly jamming attacks. XGBoost achieved the highest accuracy (0.99) and perfect detection (1.00) of normal traffic and signal drift, outperforming Random Forest (0.98), Support Vector Machine (0.97), Logistic Regression (0.93), and K Nearest Neighbors (0.81). These results highlight XGBoost as a reliable solution for wireless network security. This work contributes to ongoing research on the integration of DT for comprehensive wireless network monitoring, emphasizing their potential to improve anomaly detection and resilience in next-generation communication systems.