Literature Review on Log Anomaly Detection Approaches Utilizing Online Parsing Methodology∗

Scott Lupton, Hironori Washizaki, Nobukazu Yoshioka, Yoshiaki Fukazawa

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The use of anomaly detection for log monitoring requires parsing model input features from raw, unstructured data. Log parsing methods come in many forms, but are generally categorized as being either offline or online. In this study, a systematic literature review of anomaly detection approaches utilizing online parsing methods is performed. An inventory of these approaches is taken, research gaps are explored, and suggestions for future exploration and study are presented.

Original languageEnglish
Title of host publicationProceedings - 2021 28th Asia-Pacific Software Engineering Conference, APSEC 2021
PublisherIEEE Computer Society
Pages559-563
Number of pages5
ISBN (Electronic)9781665437844
DOIs
Publication statusPublished - 2021
Event28th Asia-Pacific Software Engineering Conference, APSEC 2021 - Virtual, Online, Taiwan, Province of China
Duration: 2021 Dec 62021 Dec 9

Publication series

NameProceedings - Asia-Pacific Software Engineering Conference, APSEC
Volume2021-December
ISSN (Print)1530-1362

Conference

Conference28th Asia-Pacific Software Engineering Conference, APSEC 2021
Country/TerritoryTaiwan, Province of China
CityVirtual, Online
Period21/12/621/12/9

Keywords

  • Log parsing
  • anomaly detection
  • log template extraction
  • online algorithms

ASJC Scopus subject areas

  • Software

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