Chapter 10: Outlier Detection

In the first section of this book, we discussed anomaly detection in depth, a feature that allows us to detect unusual behavior in time series data in an unsupervised fashion. This works well when we want to detect whether one of our applications is experiencing unusual latency at a particular time or whether a host on our corporate network is transmitting an unusual number of bytes.

In this chapter, we will learn about the second unsupervised learning feature in the Elastic Stack: outlier detection, which allows us to detect unusual entities in non-time series-based indices. Some interesting applications of outlier detection could involve, for example, detecting unusual cells in a tissue sample, investigating unusual houses, or areas in a local real estate market and catching unusual binaries installed on your computer.

The outlier detection functionality in the Elastic Stack is based on an ensemble or a grouping of four different outlier detection...