发表机构
King Fahd University of Petroleum and Minerals; IRC for Intelligent Secure Systems（法赫德国王石油与矿产大学; 智能安全系统研究中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究自动驾驶网络中因意图因果耦合导致故障难辨的问题，提出MILD框架，通过教师增强的专家混合架构及混合目标优化意图故障预测与根本原因归因，经多环境评估，该框架能实现高故障检测率等，推动下一代自主网络闭环保证。
AI 中文摘要
自动驾驶网络的愿景是在最少人工干预下进行监测、推理和行动，这依赖紧密耦合的监测、分析和驱动功能。本文将这些功能视为三个操作宏观意图，把每个功能的健康状况形式化为网络必须持续满足的意图。关键挑战在于意图间的因果耦合，单一故障会引发共同漂移并导致级联异常，现有方法难以区分真正的根本原因意图。为此引入MILD框架，从被动漂移检测重新制定意图保证为主动故障预测。基于自动驾驶控制回路的三宏观意图公式，MILD采用教师增强的专家混合架构，通过混合目标联合优化意图故障预测和根本原因归因。通过SHAP可解释性实现KPI级诊断，通过多时间范围建模进行动态意图故障紧迫性估计。在三种不同现实环境中的广泛评估表明，MILD实现了高故障检测率、强大的修复提前期和准确的意图级根本原因消歧，使其成为下一代自主网络中闭环保证的实用推动者。
英文摘要
The vision of self-driving networks that monitor, reason, and act upon themselves with minimal human intervention relies on tightly coupled monitoring, analytics, and actuation functions. In this work, we treat these functions as three operational macro-intents: continuous telemetry, real-time analytics, and programmatic actuation, and formalize the health of each function as an intent that the network must continuously satisfy. A critical, yet underexplored, challenge stems from the causal coupling among these intents, where a singular fault within one macro-intent propagates as a co-drift and subsequently triggers cascading, symptomatic anomalies across the remaining intents. This ambiguity makes it exceedingly difficult for existing, reactive approaches to distinguish the true root-cause intent from symptomatic victim intents, and their reliance on threshold-crossing detection leaves insufficient time for proactive remediation. We introduce MILD, a novel framework that reformulates intent assurance from reactive drift detection to proactive failure prediction. Grounded in our three-macro-intent formulation of the self-driving control loop, MILD employs a teacher-augmented Mixture-of-Experts architecture with a hybrid objective that jointly optimizes intent failure prediction and root-cause attribution. MILD enables KPI-level diagnostics via SHAP explainability and dynamic intent failure urgency estimation via multi-horizon modeling. Our extensive evaluation of MILD across three environments of increasing realism, from a controlled statistical benchmark, to a microservices application, to an SDN-based edge-to-cloud testbed, demonstrates that MILD achieves high failure detection rates, strong remediation lead times, and accurate intent-level root-cause disambiguation. This positions MILD as a practical enabler of closed-loop assurance in next-generation autonomous networks.
CommentsUnder review in IEEE Transactions on Network and Service Management
