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)

Recovering Transient Database Records

In this chapter, we will revisit SQLite databases and examine a type of journaling file called a Write Ahead Log (WAL). Due to the complexity of the underlying structure, parsing a WAL file is a more difficult task than our previous work with SQLite databases. There are no existing modules that we can leverage to directly interact with the WAL file in the same way we used sqlite3 or peewee with SQLite databases. Instead, we'll rely on the struct library and our ability to understand binary files.

Once we've successfully parsed the WAL file, we will leverage the regular expression library, re, in Python to identify potentially relevant forensic artifacts. Lastly, we briefly introduce another method of creating progress bars using the third-party tqdm library. With a few lines of code, we'll have a functioning progress bar that...