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

Abstract

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.