Book Image

Machine Learning with the Elastic Stack

By : Rich Collier, Bahaaldine Azarmi
Book Image

Machine Learning with the Elastic Stack

By: Rich Collier, Bahaaldine Azarmi

Overview of this book

Machine Learning with the Elastic Stack is a comprehensive overview of the embedded commercial features of anomaly detection and forecasting. The book starts with installing and setting up Elastic Stack. You will perform time series analysis on varied kinds of data, such as log files, network flows, application metrics, and financial data. As you progress through the chapters, you will deploy machine learning within the Elastic Stack for logging, security, and metrics. In the concluding chapters, you will see how machine learning jobs can be automatically distributed and managed across the Elasticsearch cluster and made resilient to failure. By the end of this book, you will understand the performance aspects of incorporating machine learning within the Elastic ecosystem and create anomaly detection jobs and view results from Kibana directly.
Table of Contents (12 chapters)

Summary

In this chapter, we have learned about the challenges that modern security teams face with legacy security solutions in keeping up with complex APT, and how Elastic ML allows analysts to have an iterative investigation approach by automating some of the forensic analysis and threat hunting steps.

In the next chapter, Chapter 6, Alerting on ML Analysis, we will put a particular focus on the alerting component that comes with commercial features and walk you through how to effectively make security insights actionable.