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

Hashing is a critical component of the DFIR workflow. While most use cases of hashing are focused on integrity checking, the use of similarity analysis allows us to learn more about near matches and file relations. This process can provide insight for malware detection, identification of restricted documents in unauthorized locations, and discovery of closely related items based on content only. Through the use of third-party libraries, we're able to lean on the power behind the C languages with the flexibility of the Python interpreter and build powerful tools that are user and developer friendly. The code for this project can be downloaded from GitHub or Packt, as described in the Preface.

A fuzzy hash is a form of metadata, or data about data. Metadata also includes embedded attributes such as document editing time, image geolocation information, and source application...