Skip to Main content Skip to Navigation
New interface
Conference papers

Configuration faults detection in IP Virtual Private Networks based on machine learning

El-Heithem Mohammedi 1, 2 Emmanuel Lavinal 2 Guillaume Fleury 1 
2 IRIT-SIERA - Service IntEgration and netwoRk Administration
IRIT - Institut de recherche en informatique de Toulouse
Abstract : Network incidents are largely due to configuration errors, particularly within network service providers who manage large complex networks. Such providers offer virtual private networks to their customers to interconnect their remote sites and provide Internet access. The growing demand for virtual private networks leads service providers to search for novel scalable approaches to locate incidents arising from configuration faults. In this paper, we propose a machine learning approach that aims to locate customer connectivity issues coming from configurations errors, in a BGP/MPLS IP virtual private network architecture. We feed the learning model with valid and faulty configuration data and train it using three algorithms: decision tree, random forest and multilayer perceptron. Since failures can occur on several routers, we consider the learning problem as a supervised multi-label classification problem, where each customer router is represented by a unique label. We carry out our experiments on three network sizes containing different types of configuration errors. Results show that multi-layer perceptron has a better accuracy in detecting faults than the other algorithms, making it a potential candidate to validate offline network configurations before online deployment.
Document type :
Conference papers
Complete list of metadata
Contributor : Emmanuel Lavinal Connect in order to contact the contributor
Submitted on : Wednesday, January 20, 2021 - 3:04:10 PM
Last modification on : Monday, July 4, 2022 - 9:03:02 AM
Long-term archiving on: : Wednesday, April 21, 2021 - 6:46:37 PM


Files produced by the author(s)


  • HAL Id : hal-03031726, version 1


El-Heithem Mohammedi, Emmanuel Lavinal, Guillaume Fleury. Configuration faults detection in IP Virtual Private Networks based on machine learning. 3rd International Conference on Machine Learning for Networking (MLN 2020), Nov 2020, Virtual conference, France. ⟨hal-03031726⟩



Record views


Files downloads