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- Elsner, D., et al.
(författare)
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Multivariate unsupervised machine learning for anomaly detection in enterprise applications
- 2019
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Ingår i: Proceedings of the Annual Hawaii International Conference on System Sciences. - : IEEE Computer Society. ; , s. 5827-5836
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Konferensbidrag (refereegranskat)abstract
- 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.
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