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Cybersecurity Attacks – Red Team Strategies

You're reading from   Cybersecurity Attacks ‚Äì Red Team Strategies A practical guide to building a penetration testing program having homefield advantage

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Product type Paperback
Published in Mar 2020
Publisher Packt
ISBN-13 9781838828868
Length 524 pages
Edition 1st Edition
Tools
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Author (1):
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Johann Rehberger Johann Rehberger
Author Profile Icon Johann Rehberger
Johann Rehberger
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Table of Contents (17) Chapters Close

Preface 1. Section 1: Embracing the Red
2. Chapter 1: Establishing an Offensive Security Program FREE CHAPTER 3. Chapter 2: Managing an Offensive Security Team 4. Chapter 3: Measuring an Offensive Security Program 5. Chapter 4: Progressive Red Teaming Operations 6. Section 2: Tactics and Techniques
7. Chapter 5: Situational Awareness – Mapping Out the Homefield Using Graph Databases 8. Chapter 6: Building a Comprehensive Knowledge Graph 9. Chapter 7: Hunting for Credentials 10. Chapter 8: Advanced Credential Hunting 11. Chapter 9: Powerful Automation 12. Chapter 10: Protecting the Pen Tester 13. Chapter 11: Traps, Deceptions, and Honeypots 14. Chapter 12: Blue Team Tactics for the Red Team 15. Assessments 16. Another Book You May Enjoy

Augmenting an existing graph or building one from scratch?

If you are not yet familiar with BloodHound (https://github.com/BloodHoundAD), you should look at it. It's a great toolset to analyze and represent Windows infrastructure, and, behind the scenes, it also leverages a Neo4j graph database.

After importing data into the BloodHound database, you can augment existing nodes and edges with more relations and metadata, such as organization information, social networks, or cloud infrastructure. That is a quick approach to get some results beyond Windows infrastructure while still fitting into the overall model. Hence, if you already have a BloodHound-based graph database, consider that as a great starting point to map out your entire organization as much as possible.

Similarly, there is Cartography (https://github.com/lyft/cartography), which focuses more on cloud assets, but it also uses Neo4j for data storage. A nice experiment is merging two datasets into one. Both projects...

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