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Network Anomaly Detection Using Federated Deep Autoencoding Gaussian Mixture Model Read chapter Read first chapter

2020 | OriginalPaper | Chapter

Space-Time Pattern Extraction in Alarm Logs for Network Diagnosis

Authors : Achille Salaün, Anne Bouillard, Marc-Olivier Buob

Published in: Machine Learning for Networking

Publisher: Springer International Publishing

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Abstract

Increasing size and complexity of telecommunication networks make troubleshooting and network management more and more critical. As analyzing a log is cumbersome and time consuming, experts need tools helping them to quickly pinpoint the root cause when a problem arises. A structure called DIG-DAG able to store chain of alarms in a compact manner according to an input log has recently been proposed. Unfortunately, for large logs, this structure may be huge, and thus hardly readable for experts. To circumvent this problem, this paper proposes a framework allowing to query a DIG-DAG in order to extract patterns of interest, and a full methodology for end-to-end analysis of a log.

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previous chapter Root Cause Analysis of Reduced Accessibility in 4G Networks
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Metadata
Title
Space-Time Pattern Extraction in Alarm Logs for Network Diagnosis
Authors
Achille Salaün
Anne Bouillard
Marc-Olivier Buob
Copyright Year
2020
Book
Machine Learning for Networking

Print ISBN: 978-3-030-45777-8

Electronic ISBN: 978-3-030-45778-5

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-45778-5_10

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