EmBeD, Energy-Based anomaly Detector in the cloud, is an approach to detect anomalies at runtime based on the free energy of a Restricted Boltzmann Machine (RBM) model. The free energy is a stochastic function that can be used to efficiently score anomalies for detecting outliers. EmBeD analyzes the system behavior from raw metric data, does not require extensive training with seeded faults, and classifies the relation of anomalous behaviors with future failures with very few false positives.

This page provides the data used for evaluating EmBeD.


Cristina Monni, Mauro Pezzè and Gaetano Prisco

Related publications

  • Monni, Pezzè, Prisco, "An RBM Anomaly Detector for the Cloud", ICST 2019.

Dataset for evaluation (EmBeD-data.zip)