Book Image

Practical Threat Intelligence and Data-Driven Threat Hunting

By : Valentina Costa-Gazcón
Book Image

Practical Threat Intelligence and Data-Driven Threat Hunting

By: Valentina Costa-Gazcón

Overview of this book

Threat hunting (TH) provides cybersecurity analysts and enterprises with the opportunity to proactively defend themselves by getting ahead of threats before they can cause major damage to their business. This book is not only an introduction for those who don’t know much about the cyber threat intelligence (CTI) and TH world, but also a guide for those with more advanced knowledge of other cybersecurity fields who are looking to implement a TH program from scratch. You will start by exploring what threat intelligence is and how it can be used to detect and prevent cyber threats. As you progress, you’ll learn how to collect data, along with understanding it by developing data models. The book will also show you how to set up an environment for TH using open source tools. Later, you will focus on how to plan a hunt with practical examples, before going on to explore the MITRE ATT&CK framework. By the end of this book, you’ll have the skills you need to be able to carry out effective hunts in your own environment.
Table of Contents (21 chapters)
Section 1: Cyber Threat Intelligence
Section 2: Understanding the Adversary
Section 3: Working with a Research Environment
Section 4: Communicating to Succeed
Appendix – The State of the Hunt

Updating the hunting process

For educational purposes, we are covering the topic of documentation after exemplifying how to hunt for the adversary using Mordor datasets, but it is a good practice to document as you go so that you can keep better track of what you are doing and also make adjustments to the whole process.

As stated in the previous section, by documenting your actions, you'll identify where there is room for improvement. Threat hunting should be a continuous improvement process. If you are carrying out hunts without learning any new, valuable lessons about your environment, your data, and your methodology, you are probably not doing something right. You should always strive for more.

If you decide to follow the model presented in this chapter, most of the information relevant for this phase will come from the "lessons learned" step. In my opinion, this step is crucial and you should not skip it, but in any case, you should always reflect on the process...