Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • apa.csl
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Boot Log Anomaly Detection with K-Seen-Before
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).ORCID iD: 0000-0003-3461-7079
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
2020 (English)In: Proceedings - 2020 IEEE 44th Annual Computers, Software, and Applications Conference, COMPSAC 2020, IEEE, 2020, p. 1005-1010Conference paper, Published paper (Refereed)
Abstract [en]

Software development for embedded systems, in particular code which interacts with boot-up procedures, can pose considerable challenges. In this work we propose the K-Seen-Before (KSB) approach to detect and highlight anomalous boot log messages, thus relieving developers from repeatedly having to manually examine boot log files of 1000+ lines. We describe the KSB instance based anomaly detection system and its relation to KNN. An industrial data set related to development of high-speed networking equipment is utilized to examine the effects of the KSB parameters on the amount of detected anomalies. The obtained results highlight the utility of KSB and provide indications of suitable KSB parameter settings for obtaining an appropriate trade-off for the cognitive workload of the developer with regards to log file analysis.

Place, publisher, year, edition, pages
IEEE, 2020. p. 1005-1010
Keywords [en]
coincidentally correct, ensemble learning, Fault localization, random forests, Application programs, Economic and social effects, Embedded systems, Software design, Anomaly detection systems, Cognitive workloads, High-speed networking, Industrial datum, Log file, Log-file analysis, Parameter setting, Trade off, Anomaly detection
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kau:diva-83079DOI: 10.1109/COMPSAC48688.2020.0-140ISI: 000629086600129Scopus ID: 2-s2.0-85094136599ISBN: 9781728173030 (print)OAI: oai:DiVA.org:kau-83079DiVA, id: diva2:1530071
Conference
44th IEEE Annual Computers, Software, and Applications Conference, COMPSAC 2020, 13 July 2020 through 17 July 2020
Available from: 2021-02-21 Created: 2021-02-21 Last updated: 2021-04-30Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records

Garcia, JohanVehkajärvi, Tobias

Search in DiVA

By author/editor
Garcia, JohanVehkajärvi, Tobias
By organisation
Department of Mathematics and Computer Science (from 2013)
Computer Sciences

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 136 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • apa.csl
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf