![[2602.13672] LEAD-Drift: Real-time and Explainable Intent Drift Detection by Learning a Data-Driven Risk Score](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.13672] LEAD-Drift: Real-time and Explainable Intent Drift Detection by Learning a Data-Driven Risk Score
Summary
The LEAD-Drift framework offers a real-time solution for detecting intent drift in Intent-Based Networking (IBN), enhancing proactive network management and reducing alert noise.
Why It Matters
As networks become more complex, the ability to detect and respond to intent drift is crucial for maintaining reliability and performance. LEAD-Drift provides a data-driven approach that not only predicts potential failures earlier but also enhances operational intelligence through explainability, making it a significant advancement in network assurance.
Key Takeaways
- LEAD-Drift detects intent drift in real-time, improving proactive network management.
- The framework uses a supervised learning approach to predict risk scores effectively.
- It enhances alert accuracy, reducing noise by over 80% while maintaining lead time.
- Incorporates explainability features to identify root causes of alerts.
- Demonstrates a 17.8% improvement in average lead time for alerts compared to traditional methods.
Computer Science > Networking and Internet Architecture arXiv:2602.13672 (cs) [Submitted on 14 Feb 2026] Title:LEAD-Drift: Real-time and Explainable Intent Drift Detection by Learning a Data-Driven Risk Score Authors:Md. Kamrul Hossain, Walid Aljoby View a PDF of the paper titled LEAD-Drift: Real-time and Explainable Intent Drift Detection by Learning a Data-Driven Risk Score, by Md. Kamrul Hossain and 1 other authors View PDF HTML (experimental) Abstract: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 mult...
