EmBeD data

<b>EmBeD</b>,<i> Energy-based anomaly detector in the cloud</i>, 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. The file data.zip contains the dataset used for validating <i>EmBeD</i>.**EmBeD**，即*面向云端的基于能量的异常检测器*，是一种依托受限玻尔兹曼机（Restricted Boltzmann Machine，RBM）模型的自由能原理，实现运行时异常检测的方法。自由能是一种随机函数，可用于高效地为异常样本分配异常分值以识别离群点。EmBeD可从原始指标数据中分析系统运行行为，无需借助种子故障开展大规模训练，且能够在保持极低假阳性率的前提下，对异常行为与未来故障之间的关联关系进行分类。data.zip文件中包含了用于验证EmBeD的数据集。