让下一篇重要论文，不再错过。

原文摘要

Current research on security monitoring is increasingly focusing on machine-learning-based approaches, but caveats remain. In addition to huge computational overhead, one concern is the lack of insights into ``why" alerts are raised. Existing interpretability approaches rely on feature attribution methods that ignore dependencies among features or on causal modeling that requires extensive domain knowledge or computational resources. This work proposes XION, a method for modeling relationships among network-flow features based on benign traffic only. During detection, anomalies are identified through violations of expected feature dependencies. Further, XION supports post-alert analysis by identifying which feature relationships break, when they break along the attack timeline, and how dependency violations evolve relatively to other identified violations. XION is evaluated on standard IDS datasets and compared against an Isolation Forest (IF) baseline across multiple attack scenarios, including both volumetric and stealthier attacks. Results show that XION matches or exceeds IF recall in all evaluated scenarios, while requiring up to 7$\times$ less inference time. At the post-alert stage, the dependency violation analysis reveals temporal and structural patterns consistent with known attack behaviors, and is further evaluated against SHAP-based feature attribution of IF alerts. Together, these findings confirm that attacks indeed disrupt feature dependencies learned from benign traffic, and that these disruptions provide additional information for understanding an alert.