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)

Parsing EML files

Recipe Difficulty: Easy

Python Version: 2.7 or 3.5

Operating System: Any

The EML file format is widely used for storing email messages, as it is a structured text file that is compatible across multiple email clients. This text file stores email headers, body content, and attachment data as plain text, using base64 to encode binary data and the Quoted-Printable (QP) encoding to store content information.

Getting started

All libraries used in this script are present in Python's standard library. We will use the built-in email library to read and extract key information from the EML files.

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