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Practical Memory Forensics

You're reading from   Practical Memory Forensics Jumpstart effective forensic analysis of volatile memory

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Product type Paperback
Published in Mar 2022
Publisher Packt
ISBN-13 9781801070331
Length 304 pages
Edition 1st Edition
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Authors (2):
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Oleg Skulkin Oleg Skulkin
Author Profile Icon Oleg Skulkin
Oleg Skulkin
Svetlana Ostrovskaya Svetlana Ostrovskaya
Author Profile Icon Svetlana Ostrovskaya
Svetlana Ostrovskaya
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Table of Contents (17) Chapters Close

Preface 1. Section 1: Basics of Memory Forensics
2. Chapter 1: Why Memory Forensics? FREE CHAPTER 3. Chapter 2: Acquisition Process 4. Section 2: Windows Forensic Analysis
5. Chapter 3: Windows Memory Acquisition 6. Chapter 4: Reconstructing User Activity with Windows Memory Forensics 7. Chapter 5: Malware Detection and Analysis with Windows Memory Forensics 8. Chapter 6: Alternative Sources of Volatile Memory 9. Section 3: Linux Forensic Analysis
10. Chapter 7: Linux Memory Acquisition 11. Chapter 8: User Activity Reconstruction 12. Chapter 9: Malicious Activity Detection 13. Section 4: macOS Forensic Analysis
14. Chapter 10: MacOS Memory Acquisition 15. Chapter 11: Malware Detection and Analysis with macOS Memory Forensics 16. Other Books You May Enjoy

Summary

Creating memory dumps of Linux-based systems is a tedious process. You do not have a huge range of tools that do everything you need at the click of a button. However, there are fairly efficient solutions that, when used correctly, will help you get everything you need.

Different tools may use different methods to access memory. The most common method is to load a kernel module; however, this method requires a lot of preparation as the module must be built on a system with a distribution and kernel version similar to the target host. The same conditions are needed to create Volatility profiles, without which further analysis of the dumps would be challenging.

Several scripting solutions can automate the process of creating memory dumps and Volatility profiles, but such solutions will often work with a limited number of distributions, so it is better to test them in conditions similar to the real ones before using them.

In this chapter, we reviewed the tools that allow...

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