Book Image

Practical Threat Detection Engineering

By : Megan Roddie, Jason Deyalsingh, Gary J. Katz
5 (2)
Book Image

Practical Threat Detection Engineering

5 (2)
By: Megan Roddie, Jason Deyalsingh, Gary J. Katz

Overview of this book

Threat validation is an indispensable component of every security detection program, ensuring a healthy detection pipeline. This comprehensive detection engineering guide will serve as an introduction for those who are new to detection validation, providing valuable guidelines to swiftly bring you up to speed. The book will show you how to apply the supplied frameworks to assess, test, and validate your detection program. It covers the entire life cycle of a detection, from creation to validation, with the help of real-world examples. Featuring hands-on tutorials and projects, this guide will enable you to confidently validate the detections in your security program. This book serves as your guide to building a career in detection engineering, highlighting the essential skills and knowledge vital for detection engineers in today's landscape. By the end of this book, you’ll have developed the skills necessary to test your security detection program and strengthen your organization’s security measures.
Table of Contents (20 chapters)
1
Part 1: Introduction to Detection Engineering
5
Part 2: Detection Creation
11
Part 3: Detection Validation
14
Part 4: Metrics and Management
16
Part 5: Detection Engineering as a Career

Calculating a detection’s efficacy

Mean time to detect, discussed earlier in this chapter, provides a historical view of the effectiveness against attacks performed against the organization. We will group additional efficacy metrics into three areas: low-fidelity coverage, automated validations, and high-fidelity coverage. When we refer to coverage, we are talking about a measure of how much of the potential attack space can be detected. The attack space is defined by what you are trying to measure. It could be a single technique or multiple MITRE ATT&CK matrixes. We’ll start by looking at some low-fidelity methods of determining coverage.

Low-fidelity coverage metrics

A common low-fidelity coverage visualization is mapping your detections to a MITRE ATT&CK matrix, as shown in Figure 11.7. Each technique is colored according to the number of detections that have been created for it. This visualization is easy to produce, and many tools will automatically...