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The complexity of services and multi-layered data in 5G networks is severely affecting the accuracy of existing anomaly detection techniques in identifying the root causes of anomalies. In this paper, we propose an anomaly analysis method based on Bayesian causal model, aiming at identifying root causes by uncovering causal links across multiple layers. Experimental results demonstrate that the proposed method outperforms traditional machine learning methods across multiple evaluation metrics, which makes it effective for anomaly detection in 5G network environments.

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An Anomaly Causal Analysis Method in Complex 5G Network Environments

  • 会议论文

  • 发表日期：

    2026年

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  • Zhijian Xu,
  • Zhiwei Zhang,
  • Guiyuan Tang,
  • Yingyu Chen,
  • Yuzi Wang

摘要

The complexity of services and multi-layered data in 5G networks is severely affecting the accuracy of existing anomaly detection techniques in identifying the root causes of anomalies. In this paper, we propose an anomaly analysis method based on Bayesian causal model, aiming at identifying root causes by uncovering causal links across multiple layers. Experimental results demonstrate that the proposed method outperforms traditional machine learning methods across multiple evaluation metrics, which makes it effective for anomaly detection in 5G network environments.