Intent-Based Networking (IBN) simplifies network management, but its reliability is challenged by "intent drift", where the network's state gradually deviates from its intended goal, often leading to silent failures. Conventional approaches struggle to detect the subtle, early stages of intent drift, raising alarms only when degradation is significant and failure is imminent, which limits their effectiveness for proactive assurance. To address this, we propose LEAD-Drift, a framework that detects intent drift in real time to enable proactive failure prevention. LEAD-Drift's core contribution is reformulating intent failure detection as a supervised learning problem by training a lightweight neural network on fixed-horizon labels to predict a future risk score. The model's raw output is then smoothed with an Exponential Moving Average (EMA) and passed through a statistically tuned threshold to generate robust, real-time alerts. Furthermore, we enhance the framework with two key features for operational intelligence: a multi-horizon modeling technique for dynamic time-to-failure estimation, and per-alert explainability using SHAP to identify root-cause KPIs. Our evaluation on a time-series dataset shows LEAD-Drift provides significantly earlier warnings, improving the average lead time by 7.3 minutes (+17.8\%) compared to a distance-based baseline. It also reduces alert noise by 80.2\% compared to a weighted-KPI heuristic, with only a minor trade-off in lead time. These results demonstrate that LEAD-Drift as a highly effective, interpretable, and operationally efficient solution for proactive network assurance in IBN.

翻译：基于意图的网络（Intent-Based Networking, IBN）简化了网络管理，但其可靠性受到“意图漂移”的挑战，即网络状态逐渐偏离其预期目标，通常导致静默故障。传统方法难以检测意图漂移微妙、早期的阶段，仅在性能显著下降且故障即将发生时发出警报，这限制了其主动保障的有效性。为解决此问题，我们提出了LEAD-Drift框架，该框架实时检测意图漂移以实现主动故障预防。LEAD-Drift的核心贡献在于，通过基于固定时间范围标签训练一个轻量级神经网络来预测未来风险评分，从而将意图故障检测重新表述为一个监督学习问题。模型的原始输出随后通过指数移动平均（Exponential Moving Average, EMA）进行平滑处理，并经过统计调优的阈值以生成鲁棒的实时警报。此外，我们为该框架增强了两个关键特性以提升运维智能：用于动态故障时间估计的多时间范围建模技术，以及使用SHAP为每个警报提供可解释性以识别根本原因的关键绩效指标（KPI）。我们在一个时间序列数据集上的评估表明，与基于距离的基线方法相比，LEAD-Drift提供了显著更早的预警，将平均提前时间提高了7.3分钟（+17.8%）。与加权KPI启发式方法相比，其警报噪声降低了80.2%，仅在提前时间上略有折衷。这些结果表明，LEAD-Drift是一种用于IBN中主动网络保障的高效、可解释且运维高效的解决方案。