Multivariate Unsupervised Machine Learning for Anomaly Detection in Enterprise Applications

Date
2019-01-08
Authors
Elsner, Daniel
Aleatrati Khosroshahi, Pouya
MacCormack, Alan D.
Lagerström, Robert
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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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Big Data, Data Science and Analytics Management, Governance and Compliance, Organizational Systems and Technology, Anomaly Detection, Application Performance Management, Data Analytics, Enterprise Architecture, Machine Learning
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10 pages
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Proceedings of the 52nd Hawaii International Conference on System Sciences
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Attribution-NonCommercial-NoDerivatives 4.0 International
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