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Practical Threat Detection Engineering

You're reading from  Practical Threat Detection Engineering

Product type Book
Published in Jul 2023
Publisher Packt
ISBN-13 9781801076715
Pages 328 pages
Edition 1st Edition
Languages
Authors (3):
Megan Roddie Megan Roddie
Profile icon Megan Roddie
Jason Deyalsingh Jason Deyalsingh
Profile icon Jason Deyalsingh
Gary J. Katz Gary J. Katz
Profile icon Gary J. Katz
View More author details
Toc

Table of Contents (20) Chapters close

Preface 1. Part 1: Introduction to Detection Engineering
2. Chapter 1: Fundamentals of Detection Engineering 3. Chapter 2: The Detection Engineering Life Cycle 4. Chapter 3: Building a Detection Engineering Test Lab 5. Part 2: Detection Creation
6. Chapter 4: Detection Data Sources 7. Chapter 5: Investigating Detection Requirements 8. Chapter 6: Developing Detections Using Indicators of Compromise 9. Chapter 7: Developing Detections Using Behavioral Indicators 10. Chapter 8: Documentation and Detection Pipelines 11. Part 3: Detection Validation
12. Chapter 9: Detection Validation 13. Chapter 10: Leveraging Threat Intelligence 14. Part 4: Metrics and Management
15. Chapter 11: Performance Management 16. Part 5: Detection Engineering as a Career
17. Chapter 12: Career Guidance for Detection Engineers 18. Index 19. Other Books You May Enjoy

Looking at data source issues and challenges

Unfortunately, a lot of variabilities are involved in what data sources will be available and the quality of those data sources. We’ll touch on several of the causes of such variability and the challenges they present in the following subsections.

Completeness

The completeness of the data provided by a data source is based on the value of the attributes captured for any given event. We do not want to waste storage resources and bandwidth on data sources that won’t add value to our investigation due to the data they expose. For example, if a system provides logs showing a network connection was established but there are no details on the source/destination of the connection with contextless timestamps and ambiguous time zones, there is likely not much that can be used from that to develop a quality detection. As such, we either ignore or de-prioritize this data source.

As an additional note regarding completeness, some...

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