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<p>With the rapid development of 6G networks, anomaly detection in network edge intelligence faces significant challenges in system interpretability and trustworthiness. Although machine learning-based methods improve detection performance, their black-box nature limits reliable cybersecurity decision support. To address this, we propose a novel framework integrating causal inference with LSTM networks. Our approach first applies Random Fourier Feature transformation to eliminate nonlinear feature correlations—a prerequisite for valid causal analysis. We then quantify feature-specific causal effects using sample-weighted adjustments to ensure model stability. Furthermore, Generative Adversarial Networks generate high-quality minority-class samples to augment training data, enhancing anomaly detection accuracy. Experimental validation on two large-scale datasets demonstrates a 33.7% improvement in explainability and a 68% reduction in root-cause localization time. This work establishes a new cybersecurity paradigm for 6G edge intelligence through causal reasoning.</p>

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Causal deep learning for enhancing explainability in 6G network edge intelligence anomaly detection

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月19日

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  • Xiao Yi,
  • Zengri Zeng,
  • Ming Dai,
  • Aimei Kang,
  • Xuhui Liu,
  • Yunlian Liu

摘要

With the rapid development of 6G networks, anomaly detection in network edge intelligence faces significant challenges in system interpretability and trustworthiness. Although machine learning-based methods improve detection performance, their black-box nature limits reliable cybersecurity decision support. To address this, we propose a novel framework integrating causal inference with LSTM networks. Our approach first applies Random Fourier Feature transformation to eliminate nonlinear feature correlations—a prerequisite for valid causal analysis. We then quantify feature-specific causal effects using sample-weighted adjustments to ensure model stability. Furthermore, Generative Adversarial Networks generate high-quality minority-class samples to augment training data, enhancing anomaly detection accuracy. Experimental validation on two large-scale datasets demonstrates a 33.7% improvement in explainability and a 68% reduction in root-cause localization time. This work establishes a new cybersecurity paradigm for 6G edge intelligence through causal reasoning.