Book Image

Threat Hunting with Elastic Stack

By : Andrew Pease
5 (1)
Book Image

Threat Hunting with Elastic Stack

5 (1)
By: Andrew Pease

Overview of this book

Threat Hunting with Elastic Stack will show you how to make the best use of Elastic Security to provide optimal protection against cyber threats. With this book, security practitioners working with Kibana will be able to put their knowledge to work and detect malicious adversary activity within their contested network. You'll take a hands-on approach to learning the implementation and methodologies that will have you up and running in no time. Starting with the foundational parts of the Elastic Stack, you'll explore analytical models and how they support security response and finally leverage Elastic technology to perform defensive cyber operations. You’ll then cover threat intelligence analytical models, threat hunting concepts and methodologies, and how to leverage them in cyber operations. After you’ve mastered the basics, you’ll apply the knowledge you've gained to build and configure your own Elastic Stack, upload data, and explore that data directly as well as by using the built-in tools in the Kibana app to hunt for nefarious activities. By the end of this book, you'll be able to build an Elastic Stack for self-training or to monitor your own network and/or assets and use Kibana to monitor and hunt for adversaries within your network.
Table of Contents (18 chapters)
1
Section 1: Introduction to Threat Hunting, Analytical Models, and Hunting Methodologies
4
Section 2: Leveraging the Elastic Stack for Collection and Analysis
11
Section 3: Operationalizing Threat Hunting

Timelines

In the Detection alerts section earlier in the chapter, we discussed how to add events to the Timelines section as a query, either from the Alerts window or from the Timelines section by dragging fields onto the query panel.

There is another section in Timelines, and that is where you can write EQL queries. This is a huge benefit because the only other places that you can use the powerful EQL queries are against the Elasticsearch API or correlation detection rules.

Creating a very simple query to correlate events from the endpoint that show the cURL process starting a malicious destination domain we used in the indicator match rule:

Figure 8.53 – Correlating endpoint and Packetbeat data together

The events are color-coded to visually associate them together. The blue endpoint events go with the blue Packetbeat data, and the same goes for the red events. You can see that the sequence by syntax for the source.port is reflected in source ports of 65016 and 65017...