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.
Keywords: big data; data science and analytics management; governance and compliance; organizational systems and technology; anomaly detection; application performance management; Machine learning; enterprise architecture;
Citation:
Elsner, Daniel, Pouya Aleatrati Khosroshahi, Alan MacCormack, and Robert Lagerström. "Multivariate Unsupervised Machine Learning for Anomaly Detection in Enterprise Applications." Proceedings of the Hawaii International Conference on System Sciences 52nd (2019): 5827–5836. View Details