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)

Challenges

As alluded to in the Using the WMI section, consider expanding the script's capabilities by being able to query remote Windows hosts. Similarly, both wmi and psutil offer access to additional information that is worth collecting. Experiment with these two libraries and collect more information, especially focusing on collecting system information for non-Windows systems, which, in the current iteration of this script, is more fully supported thanks to the wmi library.

Lastly, for a more advanced challenge, consider developing a more useful storage repository to collect and query the data. It's all well and good to collect and present data in the way we have for a few systems, but how well would this scale when run across many hundreds of systems? Imagine a scenario where you deploy and run a modified version of this script against many hosts on a network and...