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Threat Hunting with Elastic Stack

You're reading from   Threat Hunting with Elastic Stack Solve complex security challenges with integrated prevention, detection, and response

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Product type Paperback
Published in Jul 2021
Publisher Packt
ISBN-13 9781801073783
Length 392 pages
Edition 1st Edition
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Author (1):
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Andrew Pease Andrew Pease
Author Profile Icon Andrew Pease
Andrew Pease
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Toc

Table of Contents (18) Chapters Close

Preface 1. Section 1: Introduction to Threat Hunting, Analytical Models, and Hunting Methodologies
2. Chapter 1: Introduction to Cyber Threat Intelligence, Analytical Models, and Frameworks FREE CHAPTER 3. Chapter 2: Hunting Concepts, Methodologies, and Techniques 4. Section 2: Leveraging the Elastic Stack for Collection and Analysis
5. Chapter 3: Introduction to the Elastic Stack 6. Chapter 4: Building Your Hunting Lab – Part 1 7. Chapter 5: Building Your Hunting Lab – Part 2 8. Chapter 6: Data Collection with Beats and Elastic Agent 9. Chapter 7: Using Kibana to Explore and Visualize Data 10. Chapter 8: The Elastic Security App 11. Section 3: Operationalizing Threat Hunting
12. Chapter 9: Using Kibana to Pivot Through Data to Find Adversaries 13. Chapter 10: Leveraging Hunting to Inform Operations 14. Chapter 11: Enriching Data to Make Intelligence 15. Chapter 12: Sharing Information and Analysis 16. Assessments 17. Other Books You May Enjoy

Summary

In this chapter, we built on the concepts of cyber threat intelligence from the previous chapter and were introduced to threat hunting. In exploring threat hunting, we discussed various models used to frustrate the adversary and interact with other analysts, operators, and infrastructure teams; data profiling exercises to understand what data you are being presented with (and maybe what data you're missing); and how the data pattern of life can be observed and managed.

Looking back at what was introduced in this chapter, there are a lot of theory, concepts, and critical thinking methodology and we have to ask why? Why are we spending so much time pontificating about models and data patterns and pipelines? It's because we're trying to make the adversary pay for every bit that they attempt to put into your network and make the adversary pay for every bit they attempt to get out. Success means that we drive the mean time to detect and mean time to respond as close...

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