Summary
In this chapter, we looked at applying supervised and unsupervised machine learning techniques on data in Elasticsearch for various use cases.
First, we explored the use of unsupervised learning to look for anomalous behavior in time series data. We used single-metric, multi-metric, and population jobs to analyze a dataset of web application logs to look for potentially malicious activity.
Next, we looked at the use of supervised learning to train a machine learning model for classifying to classify requests to the web application as malicious using features in the request (primarily the HTTP request/response size values).
Finally, we looked at how the inference processor in ingest pipelines can be used to run continuous inference using a trained model for new data.
In the next chapter, we will move our focus to Beats and their role in the data pipeline. We will look at how different types of events can be collected by Beats agents and sent to Elasticsearch or Logstash...
