Please use this identifier to cite or link to this item: http://hdl.handle.net/10125/60018

Multivariate Unsupervised Machine Learning for Anomaly Detection in Enterprise Applications

File Size Format  
0578.pdf 585.16 kB Adobe PDF View/Open

Item Summary

Title:Multivariate Unsupervised Machine Learning for Anomaly Detection in Enterprise Applications
Authors:Elsner, Daniel
Aleatrati Khosroshahi, Pouya
MacCormack, Alan D.
Lagerström, Robert
Keywords: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
Date Issued:08 Jan 2019
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.
Pages/Duration:10 pages
URI:http://hdl.handle.net/10125/60018
ISBN:978-0-9981331-2-6
DOI:10.24251/HICSS.2019.703
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
Appears in Collections: Big Data, Data Science and Analytics Management, Governance and Compliance


Please email libraryada-l@lists.hawaii.edu if you need this content in ADA-compliant format.

This item is licensed under a Creative Commons License Creative Commons