Designing Supervised Learning-Based Incident Management Model : Case Study: Broadband Network Service Provider

Isa, Mohammad; Albarda,

doi:10.1109/iciss48059.2019.8969821

Cited by 1 publication

(1 citation statement)

References 19 publications

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“…Some of these works target specific networks such as cellular or wireless sensor networks [10,12]. Others are more general, such as [8] in which the authors proposed a supervised learning model for incident management. Their model is composed of two submodels: a classification model to categorize network incidents and a regression model to predict the duration of incidents, both are fed by data collected from application databases related to incident handling processes (customer relationship management, network monitoring system, etc.).…”

Section: Related Workmentioning

confidence: 99%

Configuration Faults Detection in IP Virtual Private Networks Based on Machine Learning

Mohammedi

Lavinal

Fleury

2021

Machine Learning for Networking

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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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Section: Related Workmentioning

confidence: 99%