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)

Security in the field

The Elastic Stack wasn't originally designed with the security analytics use case in mind; remember, it was designed to be an efficient data store and search engine. However, it has become apparent that—similar to the logging/metrics/performance use case in IT operations—the Elastic Stack is also a very good platform to use for Security Analytics because of its ability to allow real-time access to high volumes of a variety of data. Let's see why and how the evolution of the Elastic Stack into a viable platform for security analytics has taken place.

The volume and variety of data

Before diving into how to operate against security threats with Elastic ML, let's provide a bit...