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Learning Python for Forensics

You're reading from   Learning Python for Forensics Leverage the power of Python in forensic investigations

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Product type Paperback
Published in Jan 2019
Publisher Packt
ISBN-13 9781789341690
Length 476 pages
Edition 2nd Edition
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Concepts
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Authors (2):
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Preston Miller Preston Miller
Author Profile Icon Preston Miller
Preston Miller
Chapin Bryce Chapin Bryce
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Chapin Bryce
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Table of Contents (15) Chapters Close

Preface 1. Now for Something Completely Different FREE CHAPTER 2. Python Fundamentals 3. Parsing Text Files 4. Working with Serialized Data Structures 5. Databases in Python 6. Extracting Artifacts from Binary Files 7. Fuzzy Hashing 8. The Media Age 9. Uncovering Time 10. Rapidly Triaging Systems 11. Parsing Outlook PST Containers 12. Recovering Transient Database Records 13. Coming Full Circle 14. Other Books You May Enjoy

Forensic scripting best practices

Forensic best practices play a big part in what we do and, traditionally, refer to handling or acquiring evidence. However, we've designated some forensic best practices of our own when it comes to programming, as follows:

  • Do not modify the original data you're working with
  • Work on copies of the original data
  • Comment code
  • Validate your program's results (and other application results)
  • Maintain extensive logging
  • Return output in an easy-to-analyze format (your users will thank you)

The golden rule of forensics is: strongly avoid modification of the original data. Work on a verified forensic copy whenever possible. However, this may not be an option for other disciplines, such as for incident responders where the parameters and scope varies. As always, this varies on the case and circumstances, but please keep in mind the ramifications...

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