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Practical Threat Detection Engineering

You're reading from   Practical Threat Detection Engineering A hands-on guide to planning, developing, and validating detection capabilities

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Product type Paperback
Published in Jul 2023
Publisher Packt
ISBN-13 9781801076715
Length 328 pages
Edition 1st Edition
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Authors (3):
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Megan Roddie Megan Roddie
Author Profile Icon Megan Roddie
Megan Roddie
Jason Deyalsingh Jason Deyalsingh
Author Profile Icon Jason Deyalsingh
Jason Deyalsingh
Gary J. Katz Gary J. Katz
Author Profile Icon Gary J. Katz
Gary J. Katz
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Toc

Table of Contents (20) Chapters Close

Preface 1. Part 1: Introduction to Detection Engineering
2. Chapter 1: Fundamentals of Detection Engineering FREE CHAPTER 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

Measuring the efficiency of a detection engineering program

The first metric related to efficiency to be discussed, velocity, is productivity over time. Productivity can be defined as the number of detections but in an agile methodology should be measured in terms of story points. Story points are a unit of measure that is defined by each team as a consistent way of identifying how much work is required. Using points provides the added benefit of allowing team members to consider the complexity and uniqueness of the work required to create the detection. Point estimation requires teams to first normalize the value of a story point by reviewing some sample detections together and creating a consensus view of the points required to complete the work. Once there is a common agreement on point values, team members can estimate the amount of work required to complete a task, such as the creation of a new detection or the update of an existing one. The velocity of the team can therefore...

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