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Learning Python for Forensics

You're reading from   Learning Python for Forensics Learn the art of designing, developing, and deploying innovative forensic solutions through Python

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Product type Paperback
Published in May 2016
Last Updated in Feb 2025
Publisher Packt
ISBN-13 9781783285235
Length 488 pages
Edition 1st Edition
Languages
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Authors (2):
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 Miller Miller
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Miller
Chapin Bryce Chapin Bryce
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Chapin Bryce
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Table of Contents (18) 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. Did Someone Say Keylogger? 11. Parsing Outlook PST Containers 12. Recovering Transient Database Records 13. Coming Full Circle A. Installing Python B. Python Technical Details
C. Troubleshooting Exceptions Index

Chapter 3. Parsing Text Files

Text files, usually sourced from application or service logs, are a common source of artifacts in digital investigations. Log files can be quite large or contain data that makes human review difficult. Manual examination can devolve into a series of grep searches, which may or may not be fruitful. Some text files might be supported by prebuilt software. For those that are not, we will need to develop our own solution to properly parse and extract relevant information. In this chapter, we will analyze the setupapi.dev.log file, which records device information on Windows machines. This log file is commonly analyzed in forensics to extract the first connection time of USB devices on the system. Although our focus is a single log file, note that we could replicate and improve upon this basic design to handle similarly structured files.

We will step through several iterations of the same code through this chapter. Though redundant, we encourage writing...

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