Configuration faults detection in IP Virtual Private Networks based on machine learning
| Autor: | Guillaume Fleury, El-Heithem Mohammedi, Emmanuel Lavinal |
|---|---|
| Přispěvatelé: | IMS Networks, Service IntEgration and netwoRk Administration (IRIT-SIERA), Institut de recherche en informatique de Toulouse (IRIT), Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées, Lavinal, Emmanuel |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Router
Configuration Faults Detection business.product_category computer.internet_protocol Computer science BGP/MPLS Networks Multiprotocol Label Switching 02 engineering and technology Machine learning computer.software_genre [INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI] 0202 electrical engineering electronic engineering information engineering Internet access [INFO.INFO-NI] Computer Science [cs]/Networking and Internet Architecture [cs.NI] business.industry 020206 networking & telecommunications Complex network Service provider Perceptron Virtual Private Networks Scalability Artificial intelligence business computer Private network |
| Zdroj: | Session 1-MLN 2020 3rd International Conference on Machine Learning for Networking (MLN 2020) 3rd International Conference on Machine Learning for Networking (MLN 2020), Nov 2020, Virtual conference, France Machine Learning for Networking ISBN: 9783030708658 MLN HAL |
| Popis: | International audience; 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. |
| Databáze: | OpenAIRE |
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