Book Image

Python Digital Forensics Cookbook

By : Chapin Bryce, Preston Miller
Book Image

Python Digital Forensics Cookbook

By: Chapin Bryce, Preston Miller

Overview of this book

Technology plays an increasingly large role in our daily lives and shows no sign of stopping. Now, more than ever, it is paramount that an investigator develops programming expertise to deal with increasingly large datasets. By leveraging the Python recipes explored throughout this book, we make the complex simple, quickly extracting relevant information from large datasets. You will explore, develop, and deploy Python code and libraries to provide meaningful results that can be immediately applied to your investigations. Throughout the Python Digital Forensics Cookbook, recipes include topics such as working with forensic evidence containers, parsing mobile and desktop operating system artifacts, extracting embedded metadata from documents and executables, and identifying indicators of compromise. You will also learn to integrate scripts with Application Program Interfaces (APIs) such as VirusTotal and PassiveTotal, and tools such as Axiom, Cellebrite, and EnCase. By the end of the book, you will have a sound understanding of Python and how you can use it to process artifacts in your investigations.
Table of Contents (11 chapters)

What’s in the box?!

Recipe Difficulty: Medium

Python Version: 3.5

Operating System: Any

MBOX files are often found in association with UNIX systems, Thunderbird, and Google Takeouts. These MBOX containers are text files with special formatting that split messages stored within. Since there are several formats for structuring MBOX files, our script will focus on those from Google Takeout, using the output from the prior recipe.

Getting started

All libraries used in this script are present in Python's standard library. We use the built-in mailbox library to parse the Google Takeout structured MBOX file.

To learn more about the mailbox library, visit https://docs.python.org/3/library/mailbox.html.
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