# LEAD-Drift: Real-time and Explainable Intent Drift Detection by Learning a Data-Driven Risk Score

arXiv: https://arxiv.org/abs/2602.13672 · HTML: https://mlsanity.com/paper/2602.13672/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 20 references that are in the map.
- First posted on arXiv 14 Feb 2026.

## Builds on (most cited first, 4 of 4)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Intent Assurance using LLMs guided by Intent Drift](https://mlsanity.com/paper/2402.00715/) (2024)
- [NetIntent: Leveraging Large Language Models for End-to-End Intent-Based SDN Automation](https://mlsanity.com/paper/2507.14398/) (2025)
- [Intent Profiling and Translation Through Emergent Communication](https://mlsanity.com/paper/2402.02768/) (2024)

## Built on by (most cited first, 3 of 3)

- [AI-driven Intent-Based Networking Approach for Self-configuration of Next Generation Networks](https://mlsanity.com/paper/2603.23772/) (2026)
- [Untangling Co-Drift: Proactive Multi-Intent Failure Prediction and Root-Cause Disambiguation for Self-Driving Networks](https://mlsanity.com/paper/2607.25989/) (2026)
- [SoK: Semantic Decision Engines in Network Control Loops](https://mlsanity.com/paper/2610.06425/) (2026)

## Abstract (arXiv)

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.