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Practical Threat Detection Engineering

Practical Threat Detection Engineering

By : Megan Roddie, Jason Deyalsingh, Gary J. Katz
4.7 (21)
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Practical Threat Detection Engineering

Practical Threat Detection Engineering

4.7 (21)
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)
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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

Adding data sources

Now that we know more about the data sources available to us, let’s work through a few exercises to expand the data sources in our lab. Without the visibility of the data sources we are protecting, we are unable to write detections.

Keep in mind that each data source will involve an additional virtual machine or application to generate the logs. As such, if you have a limited number of compute resources available, focus on those most relevant to your organization.

The Elastic agent simplifies the process of collecting data from different sources, depending on the type of service being hosted on an endpoint. Planning the collection of data typically starts with organizing your endpoints into logical groups, based on shared characteristics, such as operating system, location, and service being hosted. These typically align with data collection requirements. Using Fleet, we can then create policies that collect relevant data for each of these groups.

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