Multivariate unsupervised machine learning for anomaly detection in enterprise applications

• 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)