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Multivariate unsupe...
Multivariate unsupervised machine learning for anomaly detection in enterprise applications
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Elsner, D. (författare)
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Khosroshahi, P. A. (författare)
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MacCormack, A. D. (författare)
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visa fler...
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- Lagerström, Robert, 1981- (författare)
- KTH,Nätverk och systemteknik
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visa färre...
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(creator_code:org_t)
- IEEE Computer Society, 2019
- 2019
- Engelska.
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Ingår i: Proceedings of the Annual Hawaii International Conference on System Sciences. - : IEEE Computer Society. ; , s. 5827-5836
- Relaterad länk:
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https://urn.kb.se/re...
Abstract
Ämnesord
Stäng
- Existing application performance management (APM) solutions lack robust anomaly detection capabilities and root cause analysis techniques, that do not require manual efforts and domain knowledge. In this paper, we develop a density-based unsupervised machine learning model to detect anomalies within an enterprise application, based upon data from multiple APM systems. The research was conducted in collaboration with a European automotive company, using two months of live application data. We show that our model detects abnormal system behavior more reliably than a commonly used outlier detection technique and provides information for detecting root causes.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Nyckelord
- Machine learning
- People movers
- Application data
- Application performance
- Automotive companies
- Detection capability
- Domain knowledge
- Enterprise applications
- Root cause analysis
- Unsupervised machine learning
- Anomaly detection
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)