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

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.
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https://hal.archives-ouvertes.fr/hal-03031726
Contributor : Emmanuel Lavinal Connect in order to contact the contributor
Submitted on : Wednesday, January 20, 2021 - 3:04:10 PM
Last modification on : Thursday, June 10, 2021 - 3:48:44 AM
Long-term archiving on: : Wednesday, April 21, 2021 - 6:46:37 PM

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  • HAL Id : hal-03031726, version 1

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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⟩

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