Reconstruction-based anomaly detection for the cloud
Tanja Hagemann, Katerina Katsarou · 2020
The detection of anomalies in cloud metrics is an important way to identify suspicious data instances that indicate a system problem such as hardware failures, performance bottlenecks or intrusions. Yet, especially in a cloud computing infrastructure where the amount and variety of services is constantly increasing, it is getting more and more challenging to monitor and maintain the system manually. Thus, it is beneficial to use machine learning to detect anomalies at least partially in an automated way. The contribution of this paper is two-folded: firstly, we evaluate three unsupervised, reconstruction-based methods for anomaly detection (PCA, Autoencoder, LSTM-Encoder-Decoder) on the Yahoo! Webscope S5 benchmark dataset. Secondly, we compare our chosen models to a widely-used density-based approach and show that our reconstruction-based approaches outperform the related work.