In this chapter, we have seen why traditional approaches to anomy detection quickly converge to their limit, whether from a human point of view (because of the amount of information to digest); or from the technical point of view where traditional statistical methodologies generate false positives or true negatives. Then we leverage the dataset and use cases build in the previous chapter to illustrate how Kibana can be used for anomaly detection based on the unsupervised machine learning feature that Machine Learning brings to the Elastic Stack.
In the next and final chapters, we'll tackle the subject of Kibana custom plugin creation by first setting up the development environment and then implementing the plugin.
