Book Image

Learning Python for Forensics - Second Edition

By : Preston Miller, Chapin Bryce
Book Image

Learning Python for Forensics - Second Edition

By: Preston Miller, Chapin Bryce

Overview of this book

Digital forensics plays an integral role in solving complex cybercrimes and helping organizations make sense of cybersecurity incidents. This second edition of Learning Python for Forensics illustrates how Python can be used to support these digital investigations and permits the examiner to automate the parsing of forensic artifacts to spend more time examining actionable data. The second edition of Learning Python for Forensics will illustrate how to develop Python scripts using an iterative design. Further, it demonstrates how to leverage the various built-in and community-sourced forensics scripts and libraries available for Python today. This book will help strengthen your analysis skills and efficiency as you creatively solve real-world problems through instruction-based tutorials. By the end of this book, you will build a collection of Python scripts capable of investigating an array of forensic artifacts and master the skills of extracting metadata and parsing complex data structures into actionable reports. Most importantly, you will have developed a foundation upon which to build as you continue to learn Python and enhance your efficacy as an investigator.
Table of Contents (15 chapters)

Summary

In this chapter, we gained greater familiarity with common serialized structures, Bitcoin, and CSV and with working with nested lists and dictionaries. Being able to manipulate lists and dictionaries is a vital skill, as data is often stored in mixed nested structures. Remember to always use the type() method to determine what type of data you're working with.

For this script, we (the authors) played around with the JSON data structure in the Python interactive prompt before writing the script. This allowed us to understand how to traverse the data structure correctly and the best manner to do so before writing any logic. The Python interactive prompt is an excellent sandbox to implement new features or to test new code. The code for this project can be downloaded from GitHub or Packt, as described in the Preface.

In the next chapter, we'll discuss a different...