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)

Introduction

Microsoft Windows is one of the most common operating systems found on machines during forensic analysis. This has led to a large effort in the community over the past two decades to develop, share, and document artifacts deposited by this operating system for use in forensic casework.

In this chapter, we continue to look at various Windows artifacts and how to process them using Python. We will leverage the framework we developed in Chapter 8, Working with Forensic Evidence Container Recipes to process these artifacts directly from forensic acquisitions. We'll use various libyal libraries to handle the underlying processing of various files, including pyevt, pyevtx, pymsiecf, pyvshadow, and pyesedb. We'll also explore how to process prefetch files using struct and a file format table of offsets and data types of interest. Here's what we'll learn...